Water Potability.ipynb

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    "# Predicting Water Potability using Machine Learning\n",
    "\n",
    "This notebook is using different Python based Machine Learning and Data Science libraries in attempt to build a machine learning model which could predict whether the water is potable or not.\n",
    "\n",
    "We're going to take the following approach \n",
    "\n",
    "1. Problem Definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modeling\n",
    "6. Experimentation\n",
    "\n",
    "## 1. Problem Defination\n",
    "\n",
    "Access to safe drinking-water is essential to health, a basic human right and a component of effective policy for health protection. This is important as a health and development issue at a national, regional and local level. In some regions, it has been shown that investments in water supply and sanitation can yield a net economic benefit, since the reductions in adverse health effects and health care costs outweigh the costs of undertaking the interventions.\n",
    "\n",
    "So by using some parameter we will predict that if the water is potable or not.\n",
    "\n",
    "## 2. Data\n",
    "\n",
    "We have taken the data from Kaggle. https://www.kaggle.com/datasets/adityakadiwal/water-potability\n",
    "\n",
    "## 3. Evaluation \n",
    "\n",
    "> If we can reach 70% accuracy at predicting whether or not water is potable or not.\n",
    "\n",
    "## 4. Features\n",
    "\n",
    "This is where we get different information about each of the features in data.\n",
    "\n",
    "**Create Data Dictionary**\n",
    "\n",
    "1. pH value:\n",
    "PH is an important parameter in evaluating the acid–base balance of water. It is also the indicator of acidic or alkaline condition of water status. WHO has recommended maximum permissible limit of pH from 6.5 to 8.5. The current investigation ranges were 6.52–6.83 which are in the range of WHO standards.\n",
    "\n",
    "2. Hardness:\n",
    "Hardness is mainly caused by calcium and magnesium salts. These salts are dissolved from geologic deposits through which water travels. The length of time water is in contact with hardness producing material helps determine how much hardness there is in raw water. Hardness was originally defined as the capacity of water to precipitate soap caused by Calcium and Magnesium.\n",
    "\n",
    "3. Solids (Total dissolved solids - TDS):\n",
    "Water has the ability to dissolve a wide range of inorganic and some organic minerals or salts such as potassium, calcium, sodium, bicarbonates, chlorides, magnesium, sulfates etc. These minerals produced un-wanted taste and diluted color in appearance of water. This is the important parameter for the use of water. The water with high TDS value indicates that water is highly mineralized. Desirable limit for TDS is 500 mg/l and maximum limit is 1000 mg/l which prescribed for drinking purpose.\n",
    "\n",
    "4. Chloramines:\n",
    "Chlorine and chloramine are the major disinfectants used in public water systems. Chloramines are most commonly formed when ammonia is added to chlorine to treat drinking water. Chlorine levels up to 4 milligrams per liter (mg/L or 4 parts per million (ppm)) are considered safe in drinking water.\n",
    "\n",
    "5. Sulfate:\n",
    "Sulfates are naturally occurring substances that are found in minerals, soil, and rocks. They are present in ambient air, groundwater, plants, and food. The principal commercial use of sulfate is in the chemical industry. Sulfate concentration in seawater is about 2,700 milligrams per liter (mg/L). It ranges from 3 to 30 mg/L in most freshwater supplies, although much higher concentrations (1000 mg/L) are found in some geographic locations.\n",
    "\n",
    "6. Conductivity:\n",
    "Pure water is not a good conductor of electric current rather’s a good insulator. Increase in ions concentration enhances the electrical conductivity of water. Generally, the amount of dissolved solids in water determines the electrical conductivity. Electrical conductivity (EC) actually measures the ionic process of a solution that enables it to transmit current. According to WHO standards, EC value should not exceeded 400 μS/cm.\n",
    "\n",
    "7. Organic_carbon:\n",
    "Total Organic Carbon (TOC) in source waters comes from decaying natural organic matter (NOM) as well as synthetic sources. TOC is a measure of the total amount of carbon in organic compounds in pure water. According to US EPA < 2 mg/L as TOC in treated / drinking water, and < 4 mg/Lit in source water which is use for treatment.\n",
    "\n",
    "8. Trihalomethanes:\n",
    "THMs are chemicals which may be found in water treated with chlorine. The concentration of THMs in drinking water varies according to the level of organic material in the water, the amount of chlorine required to treat the water, and the temperature of the water that is being treated. THM levels up to 80 ppm is considered safe in drinking water.\n",
    "\n",
    "9. Turbidity:\n",
    "The turbidity of water depends on the quantity of solid matter present in the suspended state. It is a measure of light emitting properties of water and the test is used to indicate the quality of waste discharge with respect to colloidal matter. The mean turbidity value obtained for Wondo Genet Campus (0.98 NTU) is lower than the WHO recommended value of 5.00 NTU.\n",
    "\n",
    "10. Potability:\n",
    "Indicates if water is safe for human consumption where 1 means Potable and 0 means Not potable."
   ]
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   "source": [
    "## Preparing Tools for our Machine Learning Model\n",
    "\n",
    "In this project, we're going to use Pandas, Numpy and Matplotlib for Data Manipulation"
   ]
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  {
   "cell_type": "code",
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   "id": "7b4258d3",
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   "source": [
    "# Importing tools\n",
    "# Regular EDA (exploratory data analysis) and plotting libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#We want our plots to appear inside the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Models from Scikit-Learn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Model Evaluations\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import precision_score, recall_score,f1_score\n",
    "from sklearn.metrics import plot_roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b1f3fc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Data\n",
    "df=pd.read_csv(\"water_potability.csv\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "id": "3bd9a0e9",
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       "            ph    Hardness        Solids  Chloramines     Sulfate  \\\n",
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    {
     "ename": "AttributeError",
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     "output_type": "error",
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      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
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      "File \u001b[1;32mD:\\anaconda\\anaconda.files\\lib\\site-packages\\pandas\\core\\generic.py:5575\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5568\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   5569\u001b[0m     name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[0;32m   5570\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[0;32m   5571\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[0;32m   5572\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[0;32m   5573\u001b[0m ):\n\u001b[0;32m   5574\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[1;32m-> 5575\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'tonumpy'"
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   "source": [
    "df.toPnumpy()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1a29f19a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0    1998\n",
       "1    1278\n",
       "Name: Potability, dtype: int64"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['Potability'].value_counts()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "79a21e22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping Rows With Null Values\n",
    "df.dropna(inplace=True)"
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8343d70b",
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       "      <td>4.075075</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.584087</td>\n",
       "      <td>188.313324</td>\n",
       "      <td>28748.687739</td>\n",
       "      <td>7.544869</td>\n",
       "      <td>326.678363</td>\n",
       "      <td>280.467916</td>\n",
       "      <td>8.399735</td>\n",
       "      <td>54.917862</td>\n",
       "      <td>2.559708</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.223862</td>\n",
       "      <td>248.071735</td>\n",
       "      <td>28749.716544</td>\n",
       "      <td>7.513408</td>\n",
       "      <td>393.663396</td>\n",
       "      <td>283.651634</td>\n",
       "      <td>13.789695</td>\n",
       "      <td>84.603556</td>\n",
       "      <td>2.672989</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.635849</td>\n",
       "      <td>203.361523</td>\n",
       "      <td>13672.091764</td>\n",
       "      <td>4.563009</td>\n",
       "      <td>303.309771</td>\n",
       "      <td>474.607645</td>\n",
       "      <td>12.363817</td>\n",
       "      <td>62.798309</td>\n",
       "      <td>4.401425</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>8.989900</td>\n",
       "      <td>215.047358</td>\n",
       "      <td>15921.412018</td>\n",
       "      <td>6.297312</td>\n",
       "      <td>312.931022</td>\n",
       "      <td>390.410231</td>\n",
       "      <td>9.899115</td>\n",
       "      <td>55.069304</td>\n",
       "      <td>4.613843</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>6.702547</td>\n",
       "      <td>207.321086</td>\n",
       "      <td>17246.920347</td>\n",
       "      <td>7.708117</td>\n",
       "      <td>304.510230</td>\n",
       "      <td>329.266002</td>\n",
       "      <td>16.217303</td>\n",
       "      <td>28.878601</td>\n",
       "      <td>3.442983</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>11.491011</td>\n",
       "      <td>94.812545</td>\n",
       "      <td>37188.826022</td>\n",
       "      <td>9.263166</td>\n",
       "      <td>258.930600</td>\n",
       "      <td>439.893618</td>\n",
       "      <td>16.172755</td>\n",
       "      <td>41.558501</td>\n",
       "      <td>4.369264</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>6.069616</td>\n",
       "      <td>186.659040</td>\n",
       "      <td>26138.780191</td>\n",
       "      <td>7.747547</td>\n",
       "      <td>345.700257</td>\n",
       "      <td>415.886955</td>\n",
       "      <td>12.067620</td>\n",
       "      <td>60.419921</td>\n",
       "      <td>3.669712</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>4.668102</td>\n",
       "      <td>193.681735</td>\n",
       "      <td>47580.991603</td>\n",
       "      <td>7.166639</td>\n",
       "      <td>359.948574</td>\n",
       "      <td>526.424171</td>\n",
       "      <td>13.894419</td>\n",
       "      <td>66.687695</td>\n",
       "      <td>4.435821</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2011 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ph    Hardness        Solids  Chloramines     Sulfate  \\\n",
       "0      8.316766  214.373394  22018.417441     8.059332  356.886136   \n",
       "1      9.092223  181.101509  17978.986339     6.546600  310.135738   \n",
       "2      5.584087  188.313324  28748.687739     7.544869  326.678363   \n",
       "3     10.223862  248.071735  28749.716544     7.513408  393.663396   \n",
       "4      8.635849  203.361523  13672.091764     4.563009  303.309771   \n",
       "...         ...         ...           ...          ...         ...   \n",
       "2006   8.989900  215.047358  15921.412018     6.297312  312.931022   \n",
       "2007   6.702547  207.321086  17246.920347     7.708117  304.510230   \n",
       "2008  11.491011   94.812545  37188.826022     9.263166  258.930600   \n",
       "2009   6.069616  186.659040  26138.780191     7.747547  345.700257   \n",
       "2010   4.668102  193.681735  47580.991603     7.166639  359.948574   \n",
       "\n",
       "      Conductivity  Organic_carbon  Trihalomethanes  Turbidity  Potability  \n",
       "0       363.266516       18.436524       100.341674   4.628771           0  \n",
       "1       398.410813       11.558279        31.997993   4.075075           0  \n",
       "2       280.467916        8.399735        54.917862   2.559708           0  \n",
       "3       283.651634       13.789695        84.603556   2.672989           0  \n",
       "4       474.607645       12.363817        62.798309   4.401425           0  \n",
       "...            ...             ...              ...        ...         ...  \n",
       "2006    390.410231        9.899115        55.069304   4.613843           1  \n",
       "2007    329.266002       16.217303        28.878601   3.442983           1  \n",
       "2008    439.893618       16.172755        41.558501   4.369264           1  \n",
       "2009    415.886955       12.067620        60.419921   3.669712           1  \n",
       "2010    526.424171       13.894419        66.687695   4.435821           1  \n",
       "\n",
       "[2011 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reseting the index after dropping null values\n",
    "df.reset_index(inplace = True, drop = True)\n",
    "#df.drop('level_0',axis=1,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "37429fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"water-potability11.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1508c848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ph</th>\n",
       "      <th>Hardness</th>\n",
       "      <th>Solids</th>\n",
       "      <th>Chloramines</th>\n",
       "      <th>Sulfate</th>\n",
       "      <th>Conductivity</th>\n",
       "      <th>Organic_carbon</th>\n",
       "      <th>Trihalomethanes</th>\n",
       "      <th>Turbidity</th>\n",
       "      <th>Potability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.316766</td>\n",
       "      <td>214.373394</td>\n",
       "      <td>22018.417441</td>\n",
       "      <td>8.059332</td>\n",
       "      <td>356.886136</td>\n",
       "      <td>363.266516</td>\n",
       "      <td>18.436524</td>\n",
       "      <td>100.341674</td>\n",
       "      <td>4.628771</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.092223</td>\n",
       "      <td>181.101509</td>\n",
       "      <td>17978.986339</td>\n",
       "      <td>6.546600</td>\n",
       "      <td>310.135738</td>\n",
       "      <td>398.410813</td>\n",
       "      <td>11.558279</td>\n",
       "      <td>31.997993</td>\n",
       "      <td>4.075075</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.584087</td>\n",
       "      <td>188.313324</td>\n",
       "      <td>28748.687739</td>\n",
       "      <td>7.544869</td>\n",
       "      <td>326.678363</td>\n",
       "      <td>280.467916</td>\n",
       "      <td>8.399735</td>\n",
       "      <td>54.917862</td>\n",
       "      <td>2.559708</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.223862</td>\n",
       "      <td>248.071735</td>\n",
       "      <td>28749.716544</td>\n",
       "      <td>7.513408</td>\n",
       "      <td>393.663396</td>\n",
       "      <td>283.651634</td>\n",
       "      <td>13.789695</td>\n",
       "      <td>84.603556</td>\n",
       "      <td>2.672989</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.635849</td>\n",
       "      <td>203.361523</td>\n",
       "      <td>13672.091764</td>\n",
       "      <td>4.563009</td>\n",
       "      <td>303.309771</td>\n",
       "      <td>474.607645</td>\n",
       "      <td>12.363817</td>\n",
       "      <td>62.798309</td>\n",
       "      <td>4.401425</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ph    Hardness        Solids  Chloramines     Sulfate  Conductivity  \\\n",
       "0   8.316766  214.373394  22018.417441     8.059332  356.886136    363.266516   \n",
       "1   9.092223  181.101509  17978.986339     6.546600  310.135738    398.410813   \n",
       "2   5.584087  188.313324  28748.687739     7.544869  326.678363    280.467916   \n",
       "3  10.223862  248.071735  28749.716544     7.513408  393.663396    283.651634   \n",
       "4   8.635849  203.361523  13672.091764     4.563009  303.309771    474.607645   \n",
       "\n",
       "   Organic_carbon  Trihalomethanes  Turbidity  Potability  \n",
       "0       18.436524       100.341674   4.628771           0  \n",
       "1       11.558279        31.997993   4.075075           0  \n",
       "2        8.399735        54.917862   2.559708           0  \n",
       "3       13.789695        84.603556   2.672989           0  \n",
       "4       12.363817        62.798309   4.401425           0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First 5 rows of DataFrame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9aadbfc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ph</th>\n",
       "      <th>Hardness</th>\n",
       "      <th>Solids</th>\n",
       "      <th>Chloramines</th>\n",
       "      <th>Sulfate</th>\n",
       "      <th>Conductivity</th>\n",
       "      <th>Organic_carbon</th>\n",
       "      <th>Trihalomethanes</th>\n",
       "      <th>Turbidity</th>\n",
       "      <th>Potability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>8.989900</td>\n",
       "      <td>215.047358</td>\n",
       "      <td>15921.412018</td>\n",
       "      <td>6.297312</td>\n",
       "      <td>312.931022</td>\n",
       "      <td>390.410231</td>\n",
       "      <td>9.899115</td>\n",
       "      <td>55.069304</td>\n",
       "      <td>4.613843</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>6.702547</td>\n",
       "      <td>207.321086</td>\n",
       "      <td>17246.920347</td>\n",
       "      <td>7.708117</td>\n",
       "      <td>304.510230</td>\n",
       "      <td>329.266002</td>\n",
       "      <td>16.217303</td>\n",
       "      <td>28.878601</td>\n",
       "      <td>3.442983</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>11.491011</td>\n",
       "      <td>94.812545</td>\n",
       "      <td>37188.826022</td>\n",
       "      <td>9.263166</td>\n",
       "      <td>258.930600</td>\n",
       "      <td>439.893618</td>\n",
       "      <td>16.172755</td>\n",
       "      <td>41.558501</td>\n",
       "      <td>4.369264</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>6.069616</td>\n",
       "      <td>186.659040</td>\n",
       "      <td>26138.780191</td>\n",
       "      <td>7.747547</td>\n",
       "      <td>345.700257</td>\n",
       "      <td>415.886955</td>\n",
       "      <td>12.067620</td>\n",
       "      <td>60.419921</td>\n",
       "      <td>3.669712</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>4.668102</td>\n",
       "      <td>193.681735</td>\n",
       "      <td>47580.991603</td>\n",
       "      <td>7.166639</td>\n",
       "      <td>359.948574</td>\n",
       "      <td>526.424171</td>\n",
       "      <td>13.894419</td>\n",
       "      <td>66.687695</td>\n",
       "      <td>4.435821</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ph    Hardness        Solids  Chloramines     Sulfate  \\\n",
       "2006   8.989900  215.047358  15921.412018     6.297312  312.931022   \n",
       "2007   6.702547  207.321086  17246.920347     7.708117  304.510230   \n",
       "2008  11.491011   94.812545  37188.826022     9.263166  258.930600   \n",
       "2009   6.069616  186.659040  26138.780191     7.747547  345.700257   \n",
       "2010   4.668102  193.681735  47580.991603     7.166639  359.948574   \n",
       "\n",
       "      Conductivity  Organic_carbon  Trihalomethanes  Turbidity  Potability  \n",
       "2006    390.410231        9.899115        55.069304   4.613843           1  \n",
       "2007    329.266002       16.217303        28.878601   3.442983           1  \n",
       "2008    439.893618       16.172755        41.558501   4.369264           1  \n",
       "2009    415.886955       12.067620        60.419921   3.669712           1  \n",
       "2010    526.424171       13.894419        66.687695   4.435821           1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Last 5 rows of DataFrame\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f04c47e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1200\n",
       "1     811\n",
       "Name: Potability, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Potability'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f384c7e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Potability'].value_counts().plot(kind='bar',color=['salmon','lightblue'],figsize=(10,6));\n",
    "plt.title('Potability Of Water')\n",
    "plt.xlabel(\"0 = Not Potable , 1 = Potable\")\n",
    "plt.ylabel(\"Amount\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4109c022",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2011 entries, 0 to 2010\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   ph               2011 non-null   float64\n",
      " 1   Hardness         2011 non-null   float64\n",
      " 2   Solids           2011 non-null   float64\n",
      " 3   Chloramines      2011 non-null   float64\n",
      " 4   Sulfate          2011 non-null   float64\n",
      " 5   Conductivity     2011 non-null   float64\n",
      " 6   Organic_carbon   2011 non-null   float64\n",
      " 7   Trihalomethanes  2011 non-null   float64\n",
      " 8   Turbidity        2011 non-null   float64\n",
      " 9   Potability       2011 non-null   int64  \n",
      "dtypes: float64(9), int64(1)\n",
      "memory usage: 157.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4d7c5f68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ph</th>\n",
       "      <th>Hardness</th>\n",
       "      <th>Solids</th>\n",
       "      <th>Chloramines</th>\n",
       "      <th>Sulfate</th>\n",
       "      <th>Conductivity</th>\n",
       "      <th>Organic_carbon</th>\n",
       "      <th>Trihalomethanes</th>\n",
       "      <th>Turbidity</th>\n",
       "      <th>Potability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.085990</td>\n",
       "      <td>195.968072</td>\n",
       "      <td>21917.441374</td>\n",
       "      <td>7.134338</td>\n",
       "      <td>333.224672</td>\n",
       "      <td>426.526409</td>\n",
       "      <td>14.357709</td>\n",
       "      <td>66.400859</td>\n",
       "      <td>3.969729</td>\n",
       "      <td>0.403282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.573337</td>\n",
       "      <td>32.635085</td>\n",
       "      <td>8642.239815</td>\n",
       "      <td>1.584820</td>\n",
       "      <td>41.205172</td>\n",
       "      <td>80.712572</td>\n",
       "      <td>3.324959</td>\n",
       "      <td>16.077109</td>\n",
       "      <td>0.780346</td>\n",
       "      <td>0.490678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.227499</td>\n",
       "      <td>73.492234</td>\n",
       "      <td>320.942611</td>\n",
       "      <td>1.390871</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>201.619737</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>8.577013</td>\n",
       "      <td>1.450000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.089723</td>\n",
       "      <td>176.744938</td>\n",
       "      <td>15615.665390</td>\n",
       "      <td>6.138895</td>\n",
       "      <td>307.632511</td>\n",
       "      <td>366.680307</td>\n",
       "      <td>12.124105</td>\n",
       "      <td>55.952664</td>\n",
       "      <td>3.442915</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.027297</td>\n",
       "      <td>197.191839</td>\n",
       "      <td>20933.512750</td>\n",
       "      <td>7.143907</td>\n",
       "      <td>332.232177</td>\n",
       "      <td>423.455906</td>\n",
       "      <td>14.322019</td>\n",
       "      <td>66.542198</td>\n",
       "      <td>3.968177</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.052969</td>\n",
       "      <td>216.441070</td>\n",
       "      <td>27182.587067</td>\n",
       "      <td>8.109726</td>\n",
       "      <td>359.330555</td>\n",
       "      <td>482.373169</td>\n",
       "      <td>16.683049</td>\n",
       "      <td>77.291925</td>\n",
       "      <td>4.514175</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>317.338124</td>\n",
       "      <td>56488.672413</td>\n",
       "      <td>13.127000</td>\n",
       "      <td>481.030642</td>\n",
       "      <td>753.342620</td>\n",
       "      <td>27.006707</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>6.494749</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ph     Hardness        Solids  Chloramines      Sulfate  \\\n",
       "count  2011.000000  2011.000000   2011.000000  2011.000000  2011.000000   \n",
       "mean      7.085990   195.968072  21917.441374     7.134338   333.224672   \n",
       "std       1.573337    32.635085   8642.239815     1.584820    41.205172   \n",
       "min       0.227499    73.492234    320.942611     1.390871   129.000000   \n",
       "25%       6.089723   176.744938  15615.665390     6.138895   307.632511   \n",
       "50%       7.027297   197.191839  20933.512750     7.143907   332.232177   \n",
       "75%       8.052969   216.441070  27182.587067     8.109726   359.330555   \n",
       "max      14.000000   317.338124  56488.672413    13.127000   481.030642   \n",
       "\n",
       "       Conductivity  Organic_carbon  Trihalomethanes    Turbidity   Potability  \n",
       "count   2011.000000     2011.000000      2011.000000  2011.000000  2011.000000  \n",
       "mean     426.526409       14.357709        66.400859     3.969729     0.403282  \n",
       "std       80.712572        3.324959        16.077109     0.780346     0.490678  \n",
       "min      201.619737        2.200000         8.577013     1.450000     0.000000  \n",
       "25%      366.680307       12.124105        55.952664     3.442915     0.000000  \n",
       "50%      423.455906       14.322019        66.542198     3.968177     0.000000  \n",
       "75%      482.373169       16.683049        77.291925     4.514175     1.000000  \n",
       "max      753.342620       27.006707       124.000000     6.494749     1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "695075c1",
   "metadata": {},
   "source": [
    "### Water Potability According to ph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8a5effa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ph</th>\n",
       "      <th>Hardness</th>\n",
       "      <th>Potability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.316766</td>\n",
       "      <td>214.373394</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.092223</td>\n",
       "      <td>181.101509</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.584087</td>\n",
       "      <td>188.313324</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.223862</td>\n",
       "      <td>248.071735</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.635849</td>\n",
       "      <td>203.361523</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>8.989900</td>\n",
       "      <td>215.047358</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>6.702547</td>\n",
       "      <td>207.321086</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>11.491011</td>\n",
       "      <td>94.812545</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>6.069616</td>\n",
       "      <td>186.659040</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>4.668102</td>\n",
       "      <td>193.681735</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2011 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ph    Hardness  Potability\n",
       "0      8.316766  214.373394           0\n",
       "1      9.092223  181.101509           0\n",
       "2      5.584087  188.313324           0\n",
       "3     10.223862  248.071735           0\n",
       "4      8.635849  203.361523           0\n",
       "...         ...         ...         ...\n",
       "2006   8.989900  215.047358           1\n",
       "2007   6.702547  207.321086           1\n",
       "2008  11.491011   94.812545           1\n",
       "2009   6.069616  186.659040           1\n",
       "2010   4.668102  193.681735           1\n",
       "\n",
       "[2011 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(zip(df.ph,df.Hardness,df.Potability),columns=['ph','Hardness','Potability'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "27da2840",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create another figure \n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "# Scatter with positive examples\n",
    "plt.scatter(df.ph[df.Potability==1],\n",
    "            df.Hardness[df.Potability==1],\n",
    "            c='salmon')\n",
    "\n",
    "# Scatter with Negative Results\n",
    "plt.scatter(df.ph[df.Potability==0],\n",
    "            df.Hardness[df.Potability==0],\n",
    "            c='darkblue')\n",
    "\n",
    "plt.title('Water Potability in Function of ph and Hardness',fontsize=20)\n",
    "plt.xlabel('ph',fontsize=16)\n",
    "plt.ylabel('Hardness',fontsize=16)\n",
    "plt.legend(['Water Potable','Water Not Potable']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8a71a311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.Sulfate,df.Potability).plot(kind='density',figsize=(10,6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d0f82a6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ph</th>\n",
       "      <th>Hardness</th>\n",
       "      <th>Solids</th>\n",
       "      <th>Chloramines</th>\n",
       "      <th>Sulfate</th>\n",
       "      <th>Conductivity</th>\n",
       "      <th>Organic_carbon</th>\n",
       "      <th>Trihalomethanes</th>\n",
       "      <th>Turbidity</th>\n",
       "      <th>Potability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ph</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108948</td>\n",
       "      <td>-0.087615</td>\n",
       "      <td>-0.024768</td>\n",
       "      <td>0.010524</td>\n",
       "      <td>0.014128</td>\n",
       "      <td>0.028375</td>\n",
       "      <td>0.018278</td>\n",
       "      <td>-0.035849</td>\n",
       "      <td>0.014530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hardness</th>\n",
       "      <td>0.108948</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.053269</td>\n",
       "      <td>-0.022685</td>\n",
       "      <td>-0.108521</td>\n",
       "      <td>0.011731</td>\n",
       "      <td>0.013224</td>\n",
       "      <td>-0.015400</td>\n",
       "      <td>-0.034831</td>\n",
       "      <td>-0.001505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solids</th>\n",
       "      <td>-0.087615</td>\n",
       "      <td>-0.053269</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051789</td>\n",
       "      <td>-0.162769</td>\n",
       "      <td>-0.005198</td>\n",
       "      <td>-0.005484</td>\n",
       "      <td>-0.015668</td>\n",
       "      <td>0.019409</td>\n",
       "      <td>0.040674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chloramines</th>\n",
       "      <td>-0.024768</td>\n",
       "      <td>-0.022685</td>\n",
       "      <td>-0.051789</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006254</td>\n",
       "      <td>-0.028277</td>\n",
       "      <td>-0.023808</td>\n",
       "      <td>0.014990</td>\n",
       "      <td>0.013137</td>\n",
       "      <td>0.020784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sulfate</th>\n",
       "      <td>0.010524</td>\n",
       "      <td>-0.108521</td>\n",
       "      <td>-0.162769</td>\n",
       "      <td>0.006254</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016192</td>\n",
       "      <td>0.026776</td>\n",
       "      <td>-0.023347</td>\n",
       "      <td>-0.009934</td>\n",
       "      <td>-0.015303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conductivity</th>\n",
       "      <td>0.014128</td>\n",
       "      <td>0.011731</td>\n",
       "      <td>-0.005198</td>\n",
       "      <td>-0.028277</td>\n",
       "      <td>-0.016192</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015647</td>\n",
       "      <td>0.004888</td>\n",
       "      <td>0.012495</td>\n",
       "      <td>-0.015496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Organic_carbon</th>\n",
       "      <td>0.028375</td>\n",
       "      <td>0.013224</td>\n",
       "      <td>-0.005484</td>\n",
       "      <td>-0.023808</td>\n",
       "      <td>0.026776</td>\n",
       "      <td>0.015647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005667</td>\n",
       "      <td>-0.015428</td>\n",
       "      <td>-0.015567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trihalomethanes</th>\n",
       "      <td>0.018278</td>\n",
       "      <td>-0.015400</td>\n",
       "      <td>-0.015668</td>\n",
       "      <td>0.014990</td>\n",
       "      <td>-0.023347</td>\n",
       "      <td>0.004888</td>\n",
       "      <td>-0.005667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.020497</td>\n",
       "      <td>0.009244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turbidity</th>\n",
       "      <td>-0.035849</td>\n",
       "      <td>-0.034831</td>\n",
       "      <td>0.019409</td>\n",
       "      <td>0.013137</td>\n",
       "      <td>-0.009934</td>\n",
       "      <td>0.012495</td>\n",
       "      <td>-0.015428</td>\n",
       "      <td>-0.020497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.022682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potability</th>\n",
       "      <td>0.014530</td>\n",
       "      <td>-0.001505</td>\n",
       "      <td>0.040674</td>\n",
       "      <td>0.020784</td>\n",
       "      <td>-0.015303</td>\n",
       "      <td>-0.015496</td>\n",
       "      <td>-0.015567</td>\n",
       "      <td>0.009244</td>\n",
       "      <td>0.022682</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       ph  Hardness    Solids  Chloramines   Sulfate  \\\n",
       "ph               1.000000  0.108948 -0.087615    -0.024768  0.010524   \n",
       "Hardness         0.108948  1.000000 -0.053269    -0.022685 -0.108521   \n",
       "Solids          -0.087615 -0.053269  1.000000    -0.051789 -0.162769   \n",
       "Chloramines     -0.024768 -0.022685 -0.051789     1.000000  0.006254   \n",
       "Sulfate          0.010524 -0.108521 -0.162769     0.006254  1.000000   \n",
       "Conductivity     0.014128  0.011731 -0.005198    -0.028277 -0.016192   \n",
       "Organic_carbon   0.028375  0.013224 -0.005484    -0.023808  0.026776   \n",
       "Trihalomethanes  0.018278 -0.015400 -0.015668     0.014990 -0.023347   \n",
       "Turbidity       -0.035849 -0.034831  0.019409     0.013137 -0.009934   \n",
       "Potability       0.014530 -0.001505  0.040674     0.020784 -0.015303   \n",
       "\n",
       "                 Conductivity  Organic_carbon  Trihalomethanes  Turbidity  \\\n",
       "ph                   0.014128        0.028375         0.018278  -0.035849   \n",
       "Hardness             0.011731        0.013224        -0.015400  -0.034831   \n",
       "Solids              -0.005198       -0.005484        -0.015668   0.019409   \n",
       "Chloramines         -0.028277       -0.023808         0.014990   0.013137   \n",
       "Sulfate             -0.016192        0.026776        -0.023347  -0.009934   \n",
       "Conductivity         1.000000        0.015647         0.004888   0.012495   \n",
       "Organic_carbon       0.015647        1.000000        -0.005667  -0.015428   \n",
       "Trihalomethanes      0.004888       -0.005667         1.000000  -0.020497   \n",
       "Turbidity            0.012495       -0.015428        -0.020497   1.000000   \n",
       "Potability          -0.015496       -0.015567         0.009244   0.022682   \n",
       "\n",
       "                 Potability  \n",
       "ph                 0.014530  \n",
       "Hardness          -0.001505  \n",
       "Solids             0.040674  \n",
       "Chloramines        0.020784  \n",
       "Sulfate           -0.015303  \n",
       "Conductivity      -0.015496  \n",
       "Organic_carbon    -0.015567  \n",
       "Trihalomethanes    0.009244  \n",
       "Turbidity          0.022682  \n",
       "Potability         1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation Matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eb900dfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's make our correlation matrix a little prettier\n",
    "corr_matrix=df.corr()\n",
    "fig,ax=plt.subplots(figsize=(15,10))\n",
    "ax=sns.heatmap(df.corr(), annot = True, linewidths = 0.5, linecolor = \"red\", fmt = \".4f\", ax = ax )\n",
    "ax;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "136ccc5d",
   "metadata": {},
   "source": [
    "## 5. Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4d26a533",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ph</th>\n",
       "      <th>Hardness</th>\n",
       "      <th>Solids</th>\n",
       "      <th>Chloramines</th>\n",
       "      <th>Sulfate</th>\n",
       "      <th>Conductivity</th>\n",
       "      <th>Organic_carbon</th>\n",
       "      <th>Trihalomethanes</th>\n",
       "      <th>Turbidity</th>\n",
       "      <th>Potability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.316766</td>\n",
       "      <td>214.373394</td>\n",
       "      <td>22018.417441</td>\n",
       "      <td>8.059332</td>\n",
       "      <td>356.886136</td>\n",
       "      <td>363.266516</td>\n",
       "      <td>18.436524</td>\n",
       "      <td>100.341674</td>\n",
       "      <td>4.628771</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.092223</td>\n",
       "      <td>181.101509</td>\n",
       "      <td>17978.986339</td>\n",
       "      <td>6.546600</td>\n",
       "      <td>310.135738</td>\n",
       "      <td>398.410813</td>\n",
       "      <td>11.558279</td>\n",
       "      <td>31.997993</td>\n",
       "      <td>4.075075</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.584087</td>\n",
       "      <td>188.313324</td>\n",
       "      <td>28748.687739</td>\n",
       "      <td>7.544869</td>\n",
       "      <td>326.678363</td>\n",
       "      <td>280.467916</td>\n",
       "      <td>8.399735</td>\n",
       "      <td>54.917862</td>\n",
       "      <td>2.559708</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.223862</td>\n",
       "      <td>248.071735</td>\n",
       "      <td>28749.716544</td>\n",
       "      <td>7.513408</td>\n",
       "      <td>393.663396</td>\n",
       "      <td>283.651634</td>\n",
       "      <td>13.789695</td>\n",
       "      <td>84.603556</td>\n",
       "      <td>2.672989</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.635849</td>\n",
       "      <td>203.361523</td>\n",
       "      <td>13672.091764</td>\n",
       "      <td>4.563009</td>\n",
       "      <td>303.309771</td>\n",
       "      <td>474.607645</td>\n",
       "      <td>12.363817</td>\n",
       "      <td>62.798309</td>\n",
       "      <td>4.401425</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ph    Hardness        Solids  Chloramines     Sulfate  Conductivity  \\\n",
       "0   8.316766  214.373394  22018.417441     8.059332  356.886136    363.266516   \n",
       "1   9.092223  181.101509  17978.986339     6.546600  310.135738    398.410813   \n",
       "2   5.584087  188.313324  28748.687739     7.544869  326.678363    280.467916   \n",
       "3  10.223862  248.071735  28749.716544     7.513408  393.663396    283.651634   \n",
       "4   8.635849  203.361523  13672.091764     4.563009  303.309771    474.607645   \n",
       "\n",
       "   Organic_carbon  Trihalomethanes  Turbidity  Potability  \n",
       "0       18.436524       100.341674   4.628771           0  \n",
       "1       11.558279        31.997993   4.075075           0  \n",
       "2        8.399735        54.917862   2.559708           0  \n",
       "3       13.789695        84.603556   2.672989           0  \n",
       "4       12.363817        62.798309   4.401425           0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "82f2af59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into X and y\n",
    "X=df.drop('Potability',axis=1)\n",
    "y=df['Potability']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5db3a97a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ph</th>\n",
       "      <th>Hardness</th>\n",
       "      <th>Solids</th>\n",
       "      <th>Chloramines</th>\n",
       "      <th>Sulfate</th>\n",
       "      <th>Conductivity</th>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
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       "      <td>7.544869</td>\n",
       "      <td>326.678363</td>\n",
       "      <td>280.467916</td>\n",
       "      <td>8.399735</td>\n",
       "      <td>54.917862</td>\n",
       "      <td>2.559708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.223862</td>\n",
       "      <td>248.071735</td>\n",
       "      <td>28749.716544</td>\n",
       "      <td>7.513408</td>\n",
       "      <td>393.663396</td>\n",
       "      <td>283.651634</td>\n",
       "      <td>13.789695</td>\n",
       "      <td>84.603556</td>\n",
       "      <td>2.672989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.635849</td>\n",
       "      <td>203.361523</td>\n",
       "      <td>13672.091764</td>\n",
       "      <td>4.563009</td>\n",
       "      <td>303.309771</td>\n",
       "      <td>474.607645</td>\n",
       "      <td>12.363817</td>\n",
       "      <td>62.798309</td>\n",
       "      <td>4.401425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>8.989900</td>\n",
       "      <td>215.047358</td>\n",
       "      <td>15921.412018</td>\n",
       "      <td>6.297312</td>\n",
       "      <td>312.931022</td>\n",
       "      <td>390.410231</td>\n",
       "      <td>9.899115</td>\n",
       "      <td>55.069304</td>\n",
       "      <td>4.613843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>6.702547</td>\n",
       "      <td>207.321086</td>\n",
       "      <td>17246.920347</td>\n",
       "      <td>7.708117</td>\n",
       "      <td>304.510230</td>\n",
       "      <td>329.266002</td>\n",
       "      <td>16.217303</td>\n",
       "      <td>28.878601</td>\n",
       "      <td>3.442983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>11.491011</td>\n",
       "      <td>94.812545</td>\n",
       "      <td>37188.826022</td>\n",
       "      <td>9.263166</td>\n",
       "      <td>258.930600</td>\n",
       "      <td>439.893618</td>\n",
       "      <td>16.172755</td>\n",
       "      <td>41.558501</td>\n",
       "      <td>4.369264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>6.069616</td>\n",
       "      <td>186.659040</td>\n",
       "      <td>26138.780191</td>\n",
       "      <td>7.747547</td>\n",
       "      <td>345.700257</td>\n",
       "      <td>415.886955</td>\n",
       "      <td>12.067620</td>\n",
       "      <td>60.419921</td>\n",
       "      <td>3.669712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>4.668102</td>\n",
       "      <td>193.681735</td>\n",
       "      <td>47580.991603</td>\n",
       "      <td>7.166639</td>\n",
       "      <td>359.948574</td>\n",
       "      <td>526.424171</td>\n",
       "      <td>13.894419</td>\n",
       "      <td>66.687695</td>\n",
       "      <td>4.435821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2011 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ph    Hardness        Solids  Chloramines     Sulfate  \\\n",
       "0      8.316766  214.373394  22018.417441     8.059332  356.886136   \n",
       "1      9.092223  181.101509  17978.986339     6.546600  310.135738   \n",
       "2      5.584087  188.313324  28748.687739     7.544869  326.678363   \n",
       "3     10.223862  248.071735  28749.716544     7.513408  393.663396   \n",
       "4      8.635849  203.361523  13672.091764     4.563009  303.309771   \n",
       "...         ...         ...           ...          ...         ...   \n",
       "2006   8.989900  215.047358  15921.412018     6.297312  312.931022   \n",
       "2007   6.702547  207.321086  17246.920347     7.708117  304.510230   \n",
       "2008  11.491011   94.812545  37188.826022     9.263166  258.930600   \n",
       "2009   6.069616  186.659040  26138.780191     7.747547  345.700257   \n",
       "2010   4.668102  193.681735  47580.991603     7.166639  359.948574   \n",
       "\n",
       "      Conductivity  Organic_carbon  Trihalomethanes  Turbidity  \n",
       "0       363.266516       18.436524       100.341674   4.628771  \n",
       "1       398.410813       11.558279        31.997993   4.075075  \n",
       "2       280.467916        8.399735        54.917862   2.559708  \n",
       "3       283.651634       13.789695        84.603556   2.672989  \n",
       "4       474.607645       12.363817        62.798309   4.401425  \n",
       "...            ...             ...              ...        ...  \n",
       "2006    390.410231        9.899115        55.069304   4.613843  \n",
       "2007    329.266002       16.217303        28.878601   3.442983  \n",
       "2008    439.893618       16.172755        41.558501   4.369264  \n",
       "2009    415.886955       12.067620        60.419921   3.669712  \n",
       "2010    526.424171       13.894419        66.687695   4.435821  \n",
       "\n",
       "[2011 rows x 9 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "238f30ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "2006    1\n",
       "2007    1\n",
       "2008    1\n",
       "2009    1\n",
       "2010    1\n",
       "Name: Potability, Length: 2011, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4d0547b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into training set and test set\n",
    "np.random.seed(42)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6ba02df5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ph</th>\n",
       "      <th>Hardness</th>\n",
       "      <th>Solids</th>\n",
       "      <th>Chloramines</th>\n",
       "      <th>Sulfate</th>\n",
       "      <th>Conductivity</th>\n",
       "      <th>Organic_carbon</th>\n",
       "      <th>Trihalomethanes</th>\n",
       "      <th>Turbidity</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>9.927024</td>\n",
       "      <td>208.490738</td>\n",
       "      <td>19666.992792</td>\n",
       "      <td>8.008618</td>\n",
       "      <td>340.237824</td>\n",
       "      <td>482.842435</td>\n",
       "      <td>11.360427</td>\n",
       "      <td>85.829113</td>\n",
       "      <td>4.051733</td>\n",
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       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>8.769676</td>\n",
       "      <td>215.368742</td>\n",
       "      <td>13969.438863</td>\n",
       "      <td>7.548543</td>\n",
       "      <td>322.799070</td>\n",
       "      <td>369.016667</td>\n",
       "      <td>18.919188</td>\n",
       "      <td>54.755214</td>\n",
       "      <td>3.776718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>8.077261</td>\n",
       "      <td>125.302719</td>\n",
       "      <td>23931.282833</td>\n",
       "      <td>8.773162</td>\n",
       "      <td>317.693331</td>\n",
       "      <td>398.328789</td>\n",
       "      <td>15.279583</td>\n",
       "      <td>62.668356</td>\n",
       "      <td>4.279871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>9.739562</td>\n",
       "      <td>166.948864</td>\n",
       "      <td>13623.160063</td>\n",
       "      <td>7.235922</td>\n",
       "      <td>385.059134</td>\n",
       "      <td>369.591289</td>\n",
       "      <td>12.322604</td>\n",
       "      <td>68.505852</td>\n",
       "      <td>2.568080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>5.343075</td>\n",
       "      <td>211.662091</td>\n",
       "      <td>45166.912141</td>\n",
       "      <td>6.651801</td>\n",
       "      <td>279.767500</td>\n",
       "      <td>485.959717</td>\n",
       "      <td>19.682337</td>\n",
       "      <td>70.546862</td>\n",
       "      <td>4.240032</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>7.965337</td>\n",
       "      <td>151.541889</td>\n",
       "      <td>25274.610305</td>\n",
       "      <td>7.106043</td>\n",
       "      <td>352.316182</td>\n",
       "      <td>527.688242</td>\n",
       "      <td>15.792625</td>\n",
       "      <td>52.268490</td>\n",
       "      <td>3.390982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>6.519848</td>\n",
       "      <td>183.225998</td>\n",
       "      <td>14284.647917</td>\n",
       "      <td>8.389078</td>\n",
       "      <td>373.091575</td>\n",
       "      <td>495.417954</td>\n",
       "      <td>11.638475</td>\n",
       "      <td>58.661649</td>\n",
       "      <td>3.272286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>7.615557</td>\n",
       "      <td>204.869974</td>\n",
       "      <td>8403.284552</td>\n",
       "      <td>6.012316</td>\n",
       "      <td>418.309431</td>\n",
       "      <td>311.178955</td>\n",
       "      <td>12.742329</td>\n",
       "      <td>67.282328</td>\n",
       "      <td>4.243134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>7.899452</td>\n",
       "      <td>210.734124</td>\n",
       "      <td>15896.365937</td>\n",
       "      <td>6.907203</td>\n",
       "      <td>319.886957</td>\n",
       "      <td>448.666423</td>\n",
       "      <td>18.169921</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>2.853767</td>\n",
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       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>6.854947</td>\n",
       "      <td>149.853487</td>\n",
       "      <td>16203.090697</td>\n",
       "      <td>5.734804</td>\n",
       "      <td>375.718660</td>\n",
       "      <td>350.344708</td>\n",
       "      <td>11.718895</td>\n",
       "      <td>75.498737</td>\n",
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       "<p>1608 rows × 9 columns</p>\n",
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       "            ph    Hardness        Solids  Chloramines     Sulfate  \\\n",
       "261   9.927024  208.490738  19666.992792     8.008618  340.237824   \n",
       "607   8.769676  215.368742  13969.438863     7.548543  322.799070   \n",
       "1965  8.077261  125.302719  23931.282833     8.773162  317.693331   \n",
       "240   9.739562  166.948864  13623.160063     7.235922  385.059134   \n",
       "819   5.343075  211.662091  45166.912141     6.651801  279.767500   \n",
       "...        ...         ...           ...          ...         ...   \n",
       "1130  7.965337  151.541889  25274.610305     7.106043  352.316182   \n",
       "1294  6.519848  183.225998  14284.647917     8.389078  373.091575   \n",
       "860   7.615557  204.869974   8403.284552     6.012316  418.309431   \n",
       "1459  7.899452  210.734124  15896.365937     6.907203  319.886957   \n",
       "1126  6.854947  149.853487  16203.090697     5.734804  375.718660   \n",
       "\n",
       "      Conductivity  Organic_carbon  Trihalomethanes  Turbidity  \n",
       "261     482.842435       11.360427        85.829113   4.051733  \n",
       "607     369.016667       18.919188        54.755214   3.776718  \n",
       "1965    398.328789       15.279583        62.668356   4.279871  \n",
       "240     369.591289       12.322604        68.505852   2.568080  \n",
       "819     485.959717       19.682337        70.546862   4.240032  \n",
       "...            ...             ...              ...        ...  \n",
       "1130    527.688242       15.792625        52.268490   3.390982  \n",
       "1294    495.417954       11.638475        58.661649   3.272286  \n",
       "860     311.178955       12.742329        67.282328   4.243134  \n",
       "1459    448.666423       18.169921       124.000000   2.853767  \n",
       "1126    350.344708       11.718895        75.498737   4.938999  \n",
       "\n",
       "[1608 rows x 9 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3850d3d4",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1317</th>\n",
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       "      <th>526</th>\n",
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       "      <td>18.026098</td>\n",
       "      <td>44.061625</td>\n",
       "      <td>5.400811</td>\n",
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       "      <th>393</th>\n",
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       "      <td>10.382253</td>\n",
       "      <td>78.305180</td>\n",
       "      <td>3.771314</td>\n",
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       "    <tr>\n",
       "      <th>1405</th>\n",
       "      <td>7.606067</td>\n",
       "      <td>248.041453</td>\n",
       "      <td>14609.976883</td>\n",
       "      <td>6.356555</td>\n",
       "      <td>322.356572</td>\n",
       "      <td>275.317146</td>\n",
       "      <td>11.706095</td>\n",
       "      <td>94.775244</td>\n",
       "      <td>4.581477</td>\n",
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       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>5.393346</td>\n",
       "      <td>172.550261</td>\n",
       "      <td>28099.038729</td>\n",
       "      <td>5.789669</td>\n",
       "      <td>372.621943</td>\n",
       "      <td>505.542349</td>\n",
       "      <td>17.530704</td>\n",
       "      <td>67.273874</td>\n",
       "      <td>4.633855</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>3.637171</td>\n",
       "      <td>238.943744</td>\n",
       "      <td>10102.489720</td>\n",
       "      <td>7.532192</td>\n",
       "      <td>337.534425</td>\n",
       "      <td>509.150323</td>\n",
       "      <td>13.818450</td>\n",
       "      <td>49.652834</td>\n",
       "      <td>5.446566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1474</th>\n",
       "      <td>4.881730</td>\n",
       "      <td>136.990466</td>\n",
       "      <td>19105.014903</td>\n",
       "      <td>7.264678</td>\n",
       "      <td>419.716412</td>\n",
       "      <td>358.046318</td>\n",
       "      <td>13.399392</td>\n",
       "      <td>75.934052</td>\n",
       "      <td>3.050198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>8.775486</td>\n",
       "      <td>157.354289</td>\n",
       "      <td>22113.391452</td>\n",
       "      <td>7.931503</td>\n",
       "      <td>304.807845</td>\n",
       "      <td>480.715208</td>\n",
       "      <td>15.692839</td>\n",
       "      <td>82.929345</td>\n",
       "      <td>4.393944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1767</th>\n",
       "      <td>7.710138</td>\n",
       "      <td>174.350577</td>\n",
       "      <td>31017.239418</td>\n",
       "      <td>8.212234</td>\n",
       "      <td>269.859799</td>\n",
       "      <td>437.514066</td>\n",
       "      <td>9.320040</td>\n",
       "      <td>51.545335</td>\n",
       "      <td>3.898671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624</th>\n",
       "      <td>8.000776</td>\n",
       "      <td>237.519250</td>\n",
       "      <td>18419.132883</td>\n",
       "      <td>8.323330</td>\n",
       "      <td>308.006321</td>\n",
       "      <td>396.376554</td>\n",
       "      <td>13.492536</td>\n",
       "      <td>82.753278</td>\n",
       "      <td>4.519210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>403 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ph    Hardness        Solids  Chloramines     Sulfate  \\\n",
       "1317  8.157518  236.344440  27931.208402     6.203676  325.323955   \n",
       "526   8.296344  198.197051  17468.999802     8.262904  369.590740   \n",
       "393   6.576845  156.221768  12772.095750     9.759898  341.749378   \n",
       "1405  7.606067  248.041453  14609.976883     6.356555  322.356572   \n",
       "433   5.393346  172.550261  28099.038729     5.789669  372.621943   \n",
       "...        ...         ...           ...          ...         ...   \n",
       "733   3.637171  238.943744  10102.489720     7.532192  337.534425   \n",
       "1474  4.881730  136.990466  19105.014903     7.264678  419.716412   \n",
       "692   8.775486  157.354289  22113.391452     7.931503  304.807845   \n",
       "1767  7.710138  174.350577  31017.239418     8.212234  269.859799   \n",
       "1624  8.000776  237.519250  18419.132883     8.323330  308.006321   \n",
       "\n",
       "      Conductivity  Organic_carbon  Trihalomethanes  Turbidity  \n",
       "1317    511.587664       13.381575        80.686958   4.469431  \n",
       "526     294.325142       18.026098        44.061625   5.400811  \n",
       "393     418.972467       10.382253        78.305180   3.771314  \n",
       "1405    275.317146       11.706095        94.775244   4.581477  \n",
       "433     505.542349       17.530704        67.273874   4.633855  \n",
       "...            ...             ...              ...        ...  \n",
       "733     509.150323       13.818450        49.652834   5.446566  \n",
       "1474    358.046318       13.399392        75.934052   3.050198  \n",
       "692     480.715208       15.692839        82.929345   4.393944  \n",
       "1767    437.514066        9.320040        51.545335   3.898671  \n",
       "1624    396.376554       13.492536        82.753278   4.519210  \n",
       "\n",
       "[403 rows x 9 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c257d8eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "261     0\n",
       "607     0\n",
       "1965    1\n",
       "240     1\n",
       "819     0\n",
       "       ..\n",
       "1130    0\n",
       "1294    0\n",
       "860     0\n",
       "1459    1\n",
       "1126    0\n",
       "Name: Potability, Length: 1608, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fd0137ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1317    0\n",
       "526     0\n",
       "393     0\n",
       "1405    0\n",
       "433     1\n",
       "       ..\n",
       "733     1\n",
       "1474    1\n",
       "692     1\n",
       "1767    0\n",
       "1624    0\n",
       "Name: Potability, Length: 403, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dec1ecf",
   "metadata": {},
   "source": [
    "Now we've got our data split into training and test sets, its time to build a machine learning model.\n",
    "\n",
    "We'll train it (find the patterns) on the training set.\n",
    "\n",
    "And We'll test it (use the patterns) on the test set.\n",
    "\n",
    "We're going to try 3 different machine learning models:\n",
    "1. Logistic Regression\n",
    "2. K-Nearest Neighbors Classifier\n",
    "3. Random Forest Classifier\n",
    "\n",
    "Logistic Regression \n",
    "\n",
    "The logistic regression statistic modeling technique is used when we have a binary outcome variable. For example: given the parameters, will the student pass or fail? Will it rain or not? etc. So, though we may have continuous or categorical independent variables, we can use the logistic regression modeling technique to predict the outcome when the outcome variable is binary.\n",
    "\n",
    "K-Nearest Neighbors Classifier\n",
    "\n",
    "The k-nearest neighbors algorithm, also known as KNN or k-NN, is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point.\n",
    "\n",
    "Random Forest Classifier\n",
    "\n",
    "A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "841e65fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models={\"Logistic Regression\": LogisticRegression(),\n",
    "        \"KNN\": KNeighborsClassifier(),\n",
    "        \"Random Forest Classifier\": RandomForestClassifier()}\n",
    "# Create a function to fit and score models\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluate given machine learning models.\n",
    "    models : a dict of differnt Scikit-Learn machine learning models\n",
    "    X_train : training data (no labels)\n",
    "    X_test : testing data(no labels)\n",
    "    y_train : training labels\n",
    "    y_test : test labels\n",
    "    \"\"\"\n",
    "    # Set random seed\n",
    "    np.random.seed(42)\n",
    "    # Make a dictionary to keep model scores\n",
    "    model_scores={}\n",
    "    #Loop through models\n",
    "    for name,model in models.items():\n",
    "        # Fit the model to the data\n",
    "        model.fit(X_train,y_train)\n",
    "        # Evaluate the model and append its score to model_scores\n",
    "        model_scores[name] = model.score(X_test,y_test)\n",
    "    return model_scores  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3ab1af93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.5732009925558312,\n",
       " 'KNN': 0.5409429280397022,\n",
       " 'Random Forest Classifier': 0.652605459057072}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = fit_and_score(models=models,X_train=X_train,X_test=X_test,y_train=y_train,y_test=y_test)\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6ecf586c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare=pd.DataFrame(model_scores,index=[\"accuracy\"])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "565f8781",
   "metadata": {},
   "source": [
    "Now we've got a baseline model... and we know a model's first predictions aren't always what we should based ournext steps off. What should we do?\n",
    "\n",
    "Let's look at the following:\n",
    "* Hyperparameter tuning\n",
    "* Feature importance\n",
    "* Confusion Matrix\n",
    "* Cross-Validation\n",
    "* Precision\n",
    "* Recall\n",
    "* F1 Score\n",
    "* Classification Report\n",
    "* ROC Curve\n",
    "* Area under the curve (AUC)\n",
    "\n",
    "## Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "73a17c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's tune KNN\n",
    "\n",
    "train_scores=[]\n",
    "test_scores=[]\n",
    "\n",
    "# Create a list of different values for n_neighbors\n",
    "neighbors=range(1,21)\n",
    "\n",
    "# Setup KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Loop through different n_neighbors\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)\n",
    "    \n",
    "    # Fit the algorithms\n",
    "    knn.fit(X_train,y_train)\n",
    "    \n",
    "    # Update the training scores list\n",
    "    train_scores.append(knn.score(X_train,y_train))\n",
    "    \n",
    "    # Update the test scores list\n",
    "    test_scores.append(knn.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3ca8d72c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.7549751243781094,\n",
       " 0.7481343283582089,\n",
       " 0.6902985074626866,\n",
       " 0.695273631840796,\n",
       " 0.6927860696517413,\n",
       " 0.6847014925373134,\n",
       " 0.6759950248756219,\n",
       " 0.6741293532338308,\n",
       " 0.6623134328358209,\n",
       " 0.6654228855721394,\n",
       " 0.6511194029850746,\n",
       " 0.652363184079602,\n",
       " 0.6393034825870647,\n",
       " 0.6467661691542289,\n",
       " 0.6411691542288557,\n",
       " 0.6374378109452736,\n",
       " 0.6324626865671642,\n",
       " 0.6330845771144279,\n",
       " 0.6380597014925373]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "21164ab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5583126550868487,\n",
       " 0.5856079404466501,\n",
       " 0.5235732009925558,\n",
       " 0.56575682382134,\n",
       " 0.5409429280397022,\n",
       " 0.5508684863523573,\n",
       " 0.5459057071960298,\n",
       " 0.5781637717121588,\n",
       " 0.575682382133995,\n",
       " 0.56575682382134,\n",
       " 0.5508684863523573,\n",
       " 0.5632754342431762,\n",
       " 0.5384615384615384,\n",
       " 0.5508684863523573,\n",
       " 0.5459057071960298,\n",
       " 0.5607940446650124,\n",
       " 0.543424317617866,\n",
       " 0.5558312655086849,\n",
       " 0.5359801488833746,\n",
       " 0.5558312655086849]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2d2d6cac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 58.56%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors, train_scores, label=\"Train Score\")\n",
    "plt.plot(neighbors, test_scores, label=\"Test Score\")\n",
    "plt.xticks(np.arange(1,21,1))\n",
    "plt.xlabel(\"Number of Neighbors\")\n",
    "plt.ylabel(\"Model Score\")\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Maximum KNN score on the test data: {max(test_scores)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "043814df",
   "metadata": {},
   "source": [
    "## Hyperparameters tuning with RandomizedSearchCV\n",
    "\n",
    "We're going to tune:\n",
    "* LogisticRegression\n",
    "* RandomForestClassifier()\n",
    "\n",
    "... using RandomizedSearchCmV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "df2682f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a hyperparameter grid for LogisticRegression\n",
    "log_reg_grid={\"C\":np.logspace(-5,5,25),\n",
    "              \"solver\": ['liblinear','sag']}\n",
    "\n",
    "# Create a hyperparameter grid for RandomForestClassifier\n",
    "rf_grid = {\"n_estimators\": np.arange(10,1000,30),\n",
    "           \"max_depth\": [None,3,5,7,10],\n",
    "           \"min_samples_split\": np.arange(1,20,2),\n",
    "           \"min_samples_leaf\": np.arange(1,20,2)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7209c002",
   "metadata": {},
   "source": [
    "Now we've got hyperparameter grids setup for each of our models,let's tune them using RandomizedSearchCV`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "89a7c76e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-05, 2.61015722e-05, 6.81292069e-05, 1.77827941e-04,\n",
       "       4.64158883e-04, 1.21152766e-03, 3.16227766e-03, 8.25404185e-03,\n",
       "       2.15443469e-02, 5.62341325e-02, 1.46779927e-01, 3.83118685e-01,\n",
       "       1.00000000e+00, 2.61015722e+00, 6.81292069e+00, 1.77827941e+01,\n",
       "       4.64158883e+01, 1.21152766e+02, 3.16227766e+02, 8.25404185e+02,\n",
       "       2.15443469e+03, 5.62341325e+03, 1.46779927e+04, 3.83118685e+04,\n",
       "       1.00000000e+05]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;, &#x27;sag&#x27;]},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-05, 2.61015722e-05, 6.81292069e-05, 1.77827941e-04,\n",
       "       4.64158883e-04, 1.21152766e-03, 3.16227766e-03, 8.25404185e-03,\n",
       "       2.15443469e-02, 5.62341325e-02, 1.46779927e-01, 3.83118685e-01,\n",
       "       1.00000000e+00, 2.61015722e+00, 6.81292069e+00, 1.77827941e+01,\n",
       "       4.64158883e+01, 1.21152766e+02, 3.16227766e+02, 8.25404185e+02,\n",
       "       2.15443469e+03, 5.62341325e+03, 1.46779927e+04, 3.83118685e+04,\n",
       "       1.00000000e+05]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;, &#x27;sag&#x27;]},\n",
       "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={'C': array([1.00000000e-05, 2.61015722e-05, 6.81292069e-05, 1.77827941e-04,\n",
       "       4.64158883e-04, 1.21152766e-03, 3.16227766e-03, 8.25404185e-03,\n",
       "       2.15443469e-02, 5.62341325e-02, 1.46779927e-01, 3.83118685e-01,\n",
       "       1.00000000e+00, 2.61015722e+00, 6.81292069e+00, 1.77827941e+01,\n",
       "       4.64158883e+01, 1.21152766e+02, 3.16227766e+02, 8.25404185e+02,\n",
       "       2.15443469e+03, 5.62341325e+03, 1.46779927e+04, 3.83118685e+04,\n",
       "       1.00000000e+05]),\n",
       "                                        'solver': ['liblinear', 'sag']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune LogisiticRegression\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for LogisticRegression\n",
    "rs_log_reg= RandomizedSearchCV(LogisticRegression(),\n",
    "                               param_distributions=log_reg_grid,\n",
    "                               cv=5,\n",
    "                               n_iter=20,\n",
    "                               verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model for LogisticRegression\n",
    "rs_log_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0ddb5a7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 2.6101572156825386}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2aeb12f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5732009925558312"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e5f4fb",
   "metadata": {},
   "source": [
    "Now we've tuned LogisticRegression(),let's do the same for RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "54ab4269",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:378: FitFailedWarning: \n",
      "5 fits failed out of a total of 100.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "5 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 476, in fit\n",
      "    trees = Parallel(\n",
      "  File \"C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\joblib\\parallel.py\", line 1043, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\joblib\\parallel.py\", line 861, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\joblib\\parallel.py\", line 779, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\joblib\\_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\joblib\\_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\joblib\\parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\joblib\\parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\utils\\fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 189, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\tree\\_classes.py\", line 969, in fit\n",
      "    super().fit(\n",
      "  File \"C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\tree\\_classes.py\", line 265, in fit\n",
      "    check_scalar(\n",
      "  File \"C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\utils\\validation.py\", line 1480, in check_scalar\n",
      "    raise ValueError(\n",
      "ValueError: min_samples_split == 1, must be >= 2.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the test scores are non-finite: [0.66167837 0.67037209 0.62376115 0.65172694 0.65794586 0.6231381\n",
      " 0.62313616 0.66853002 0.65546526 0.66105532 0.63991409 0.65794199\n",
      " 0.67536038 0.66104565        nan 0.62810704 0.62313616 0.62313616\n",
      " 0.64239663 0.66105145]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 7, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  40,  70, 100, 130, 160, 190, 220, 250, 280, 310, 340, 370,\n",
       "       400, 430, 460, 490, 520, 550, 580, 610, 640, 670, 700, 730, 760,\n",
       "       790, 820, 850, 880, 910, 940, 970])},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 7, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  40,  70, 100, 130, 160, 190, 220, 250, 280, 310, 340, 370,\n",
       "       400, 430, 460, 490, 520, 550, 580, 610, 640, 670, 700, 730, 760,\n",
       "       790, 820, 850, 880, 910, 940, 970])},\n",
       "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 7, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'n_estimators': array([ 10,  40,  70, 100, 130, 160, 190, 220, 250, 280, 310, 340, 370,\n",
       "       400, 430, 460, 490, 520, 550, 580, 610, 640, 670, 700, 730, 760,\n",
       "       790, 820, 850, 880, 910, 940, 970])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup Random Seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for RandomizedSearchCV\n",
    "rs_rf= RandomizedSearchCV(RandomForestClassifier(),\n",
    "                          param_distributions=rf_grid,\n",
    "                          n_iter=20,\n",
    "                          verbose=True,\n",
    "                          cv=5)\n",
    "\n",
    "# Fit random hyperparameter search model for RandomForestClassifier\n",
    "rs_rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e3a484f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 310,\n",
       " 'min_samples_split': 7,\n",
       " 'min_samples_leaf': 5,\n",
       " 'max_depth': None}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "63bfd6f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6774193548387096"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b496078f",
   "metadata": {},
   "source": [
    "## HyperTuning with GridSearchCV\n",
    "\n",
    "Since Our RandomForestClassifier model provides the best score so far so we will try to improve it more using GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "bb3cbf95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 135 candidates, totalling 675 fits\n"
     ]
    }
   ],
   "source": [
    "# Different Hyperparameters for our RandomForestClassifier model\n",
    "rf_cl_grid={\"n_estimators\": np.arange(305,315,2),\n",
    "           \"max_depth\": [None,5,7],\n",
    "           \"min_samples_split\": np.arange(4,10,2),\n",
    "           \"min_samples_leaf\": np.arange(4,10,2)}\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_rf_cl= GridSearchCV(RandomForestClassifier(),param_grid=rf_cl_grid,\n",
    "                        cv=5,\n",
    "                        verbose=True)\n",
    "\n",
    "# Fit grid hyperparameter search model\n",
    "gs_rf_cl.fit(X_train,y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "3d1293a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': None,\n",
       " 'min_samples_leaf': 4,\n",
       " 'min_samples_split': 8,\n",
       " 'n_estimators': 313}"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_rf_cl.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "ad509bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6625310173697271"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_rf_cl.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bae9b59d",
   "metadata": {},
   "source": [
    "## Evaluating our Machine Learning Model beyond accuracy\n",
    "\n",
    "* ROC curve and AUC curve \n",
    "* Confusion Matrix\n",
    "* Classification Report\n",
    "* Precision\n",
    "* Recall\n",
    "* F1-score\n",
    "\n",
    "... and it would be great if cross-validation was used where possible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "61501d14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions with tuned model\n",
    "y_preds=rs_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "597c9dda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0,\n",
       "       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
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       "       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "09df502c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1317    0\n",
       "526     0\n",
       "393     0\n",
       "1405    0\n",
       "433     1\n",
       "       ..\n",
       "733     1\n",
       "1474    1\n",
       "692     1\n",
       "1767    0\n",
       "1624    0\n",
       "Name: Potability, Length: 403, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1ddfe149",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Desktop\\Water-Potability\\env\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curve and calculate AUC metric \n",
    "plot_roc_curve(rs_rf,X_test,y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "98894f84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[203  28]\n",
      " [102  70]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "print(confusion_matrix(y_test,y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "423a8897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import Seaborn\n",
    "import seaborn as sns\n",
    "sns.set(font_scale=1.5) # Increase font size\n",
    " \n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plots a confusion matrix using Seaborn's heatmap().\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(5, 5))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                     annot=True, # Annotate the boxes\n",
    "                     cbar=False,\n",
    "                     fmt='')\n",
    "    plt.xlabel(\"Predicted label\") # predictions go on the x-axis\n",
    "    plt.ylabel(\"True label\") # true labels go on the y-axis \n",
    "    \n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8860f34",
   "metadata": {},
   "source": [
    "Now we've got a ROC curve, an AUC metric and a confusion matrix, let's get a classification report as well as cross-validated precision, recall and F1-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d3817bef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.88      0.76       231\n",
      "           1       0.71      0.41      0.52       172\n",
      "\n",
      "    accuracy                           0.68       403\n",
      "   macro avg       0.69      0.64      0.64       403\n",
      "weighted avg       0.69      0.68      0.66       403\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f50843",
   "metadata": {},
   "source": [
    "### Calculate evaluation metrics using cross-validation\n",
    "\n",
    "We're going to calculate precision, recall and f1-score of our model using cross-validation and to do so we'll be using `cross_val_score()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a00b5491",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 310,\n",
       " 'min_samples_split': 7,\n",
       " 'min_samples_leaf': 5,\n",
       " 'max_depth': None}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best hyperparamters\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "abd8dcd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier with best parameters\n",
    "clf=RandomForestClassifier(n_estimators=310,\n",
    "                           min_samples_split=7,\n",
    "                           min_samples_leaf=5,\n",
    "                           max_depth=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "414493cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.60049628, 0.65671642, 0.66915423, 0.61940299, 0.69154229])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated accuracy\n",
    "cv_acc= cross_val_score(clf,X,y,cv=5,scoring='accuracy')\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cdec3ef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6474624396627285"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc=np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5cac4f8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6157141111380586"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated precision\n",
    "cv_precision=cross_val_score(clf,X,y,cv=5,scoring='precision')\n",
    "cv_precision=np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "05e869c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35393471180792246"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated Recall\n",
    "cv_recall=cross_val_score(clf,X,y,cv=5,scoring='recall')\n",
    "cv_recall=np.mean(cv_recall)\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3379f004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4324110823940555"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated F1-score\n",
    "cv_f1=cross_val_score(clf,X,y,cv=5,scoring='f1')\n",
    "cv_f1=np.mean(cv_f1)\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f79b8af6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize cross-validated metrics \n",
    "cv_metrics = pd.DataFrame({'Accuracy': cv_acc,\n",
    "                          'Precision': cv_precision,\n",
    "                          \"Recall\": cv_recall,\n",
    "                          'F1':cv_f1},\n",
    "                          index=[0])\n",
    "cv_metrics.T.plot.bar(title='Cross-validated classification metrics',legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89cadfb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(clf,open('water-potability.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a5a3599",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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