ai6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_name = [\"Population\",\"Profit\"]  \n",
    "data = pd.read_csv(r'C:\\Users\\HP\\Downloads\\Cohort4-master\\Cohort4-master\\beginner\\machine-learning\\week02\\ex1data1.txt', names = column_name)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.159800</td>\n",
       "      <td>5.839135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.869884</td>\n",
       "      <td>5.510262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.026900</td>\n",
       "      <td>-2.680700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.707700</td>\n",
       "      <td>1.986900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.589400</td>\n",
       "      <td>4.562300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.578100</td>\n",
       "      <td>7.046700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.203000</td>\n",
       "      <td>24.147000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Population     Profit\n",
       "count   97.000000  97.000000\n",
       "mean     8.159800   5.839135\n",
       "std      3.869884   5.510262\n",
       "min      5.026900  -2.680700\n",
       "25%      5.707700   1.986900\n",
       "50%      6.589400   4.562300\n",
       "75%      8.578100   7.046700\n",
       "max     22.203000  24.147000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table id=\"T_efac6e02_e6f3_11e9_a361_acfdce6062a1\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >Population</th> \n",
       "        <th class=\"col_heading level0 col1\" >Profit</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_efac6e02_e6f3_11e9_a361_acfdce6062a1level0_row0\" class=\"row_heading level0 row0\" >Population</th> \n",
       "        <td id=\"T_efac6e02_e6f3_11e9_a361_acfdce6062a1row0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_efac6e02_e6f3_11e9_a361_acfdce6062a1row0_col1\" class=\"data row0 col1\" >0.837873</td> \n",
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       "        <th id=\"T_efac6e02_e6f3_11e9_a361_acfdce6062a1level0_row1\" class=\"row_heading level0 row1\" >Profit</th> \n",
       "        <td id=\"T_efac6e02_e6f3_11e9_a361_acfdce6062a1row1_col0\" class=\"data row1 col0\" >0.837873</td> \n",
       "        <td id=\"T_efac6e02_e6f3_11e9_a361_acfdce6062a1row1_col1\" class=\"data row1 col1\" >1</td> \n",
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     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "corr = data.corr()\n",
    "corr.style.background_gradient(cmap='Spectral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table id=\"T_f04cd668_e6f3_11e9_b0bc_acfdce6062a1\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >Population</th> \n",
       "        <th class=\"col_heading level0 col1\" >Profit</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_f04cd668_e6f3_11e9_b0bc_acfdce6062a1level0_row0\" class=\"row_heading level0 row0\" >Population</th> \n",
       "        <td id=\"T_f04cd668_e6f3_11e9_b0bc_acfdce6062a1row0_col0\" class=\"data row0 col0\" >14.976</td> \n",
       "        <td id=\"T_f04cd668_e6f3_11e9_b0bc_acfdce6062a1row0_col1\" class=\"data row0 col1\" >17.8669</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_f04cd668_e6f3_11e9_b0bc_acfdce6062a1level0_row1\" class=\"row_heading level0 row1\" >Profit</th> \n",
       "        <td id=\"T_f04cd668_e6f3_11e9_b0bc_acfdce6062a1row1_col0\" class=\"data row1 col0\" >17.8669</td> \n",
       "        <td id=\"T_f04cd668_e6f3_11e9_b0bc_acfdce6062a1row1_col1\" class=\"data row1 col1\" >30.363</td> \n",
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     "execution_count": 6,
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   ],
   "source": [
    "cov = data.cov()\n",
    "cov.style.background_gradient(cmap='Spectral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Population    Profit\n",
      "0       6.1101  17.59200\n",
      "1       5.5277   9.13020\n",
      "2       8.5186  13.66200\n",
      "3       7.0032  11.85400\n",
      "4       5.8598   6.82330\n",
      "5       8.3829  11.88600\n",
      "6       7.4764   4.34830\n",
      "7       8.5781  12.00000\n",
      "8       6.4862   6.59870\n",
      "9       5.0546   3.81660\n",
      "10      5.7107   3.25220\n",
      "11     14.1640  15.50500\n",
      "12      5.7340   3.15510\n",
      "13      8.4084   7.22580\n",
      "14      5.6407   0.71618\n",
      "15      5.3794   3.51290\n",
      "16      6.3654   5.30480\n",
      "17      5.1301   0.56077\n",
      "18      6.4296   3.65180\n",
      "19      7.0708   5.38930\n",
      "20      6.1891   3.13860\n",
      "21     20.2700  21.76700\n",
      "22      5.4901   4.26300\n",
      "23      6.3261   5.18750\n",
      "24      5.5649   3.08250\n",
      "25     18.9450  22.63800\n",
      "26     12.8280  13.50100\n",
      "27     10.9570   7.04670\n",
      "28     13.1760  14.69200\n",
      "29     22.2030  24.14700\n",
      "..         ...       ...\n",
      "67     10.2360   7.77540\n",
      "68      5.4994   1.01730\n",
      "69     20.3410  20.99200\n",
      "70     10.1360   6.67990\n",
      "71      7.3345   4.02590\n",
      "72      6.0062   1.27840\n",
      "73      7.2259   3.34110\n",
      "74      5.0269  -2.68070\n",
      "75      6.5479   0.29678\n",
      "76      7.5386   3.88450\n",
      "77      5.0365   5.70140\n",
      "78     10.2740   6.75260\n",
      "79      5.1077   2.05760\n",
      "80      5.7292   0.47953\n",
      "81      5.1884   0.20421\n",
      "82      6.3557   0.67861\n",
      "83      9.7687   7.54350\n",
      "84      6.5159   5.34360\n",
      "85      8.5172   4.24150\n",
      "86      9.1802   6.79810\n",
      "87      6.0020   0.92695\n",
      "88      5.5204   0.15200\n",
      "89      5.0594   2.82140\n",
      "90      5.7077   1.84510\n",
      "91      7.6366   4.29590\n",
      "92      5.8707   7.20290\n",
      "93      5.3054   1.98690\n",
      "94      8.2934   0.14454\n",
      "95     13.3940   9.05510\n",
      "96      5.4369   0.61705\n",
      "\n",
      "[97 rows x 2 columns]\n",
      "Datatype of X and Y :  <class 'pandas.core.frame.DataFrame'>\n",
      "\n",
      " Input Feature X =  [[ 6.1101]\n",
      " [ 5.5277]\n",
      " [ 8.5186]\n",
      " [ 7.0032]\n",
      " [ 5.8598]\n",
      " [ 8.3829]\n",
      " [ 7.4764]\n",
      " [ 8.5781]\n",
      " [ 6.4862]\n",
      " [ 5.0546]\n",
      " [ 5.7107]\n",
      " [14.164 ]\n",
      " [ 5.734 ]\n",
      " [ 8.4084]\n",
      " [ 5.6407]\n",
      " [ 5.3794]\n",
      " [ 6.3654]\n",
      " [ 5.1301]\n",
      " [ 6.4296]\n",
      " [ 7.0708]\n",
      " [ 6.1891]\n",
      " [20.27  ]\n",
      " [ 5.4901]\n",
      " [ 6.3261]\n",
      " [ 5.5649]\n",
      " [18.945 ]\n",
      " [12.828 ]\n",
      " [10.957 ]\n",
      " [13.176 ]\n",
      " [22.203 ]\n",
      " [ 5.2524]\n",
      " [ 6.5894]\n",
      " [ 9.2482]\n",
      " [ 5.8918]\n",
      " [ 8.2111]\n",
      " [ 7.9334]\n",
      " [ 8.0959]\n",
      " [ 5.6063]\n",
      " [12.836 ]\n",
      " [ 6.3534]\n",
      " [ 5.4069]\n",
      " [ 6.8825]\n",
      " [11.708 ]\n",
      " [ 5.7737]\n",
      " [ 7.8247]\n",
      " [ 7.0931]\n",
      " [ 5.0702]\n",
      " [ 5.8014]\n",
      " [11.7   ]\n",
      " [ 5.5416]\n",
      " [ 7.5402]\n",
      " [ 5.3077]\n",
      " [ 7.4239]\n",
      " [ 7.6031]\n",
      " [ 6.3328]\n",
      " [ 6.3589]\n",
      " [ 6.2742]\n",
      " [ 5.6397]\n",
      " [ 9.3102]\n",
      " [ 9.4536]\n",
      " [ 8.8254]\n",
      " [ 5.1793]\n",
      " [21.279 ]\n",
      " [14.908 ]\n",
      " [18.959 ]\n",
      " [ 7.2182]\n",
      " [ 8.2951]\n",
      " [10.236 ]\n",
      " [ 5.4994]\n",
      " [20.341 ]\n",
      " [10.136 ]\n",
      " [ 7.3345]\n",
      " [ 6.0062]\n",
      " [ 7.2259]\n",
      " [ 5.0269]\n",
      " [ 6.5479]\n",
      " [ 7.5386]\n",
      " [ 5.0365]\n",
      " [10.274 ]\n",
      " [ 5.1077]\n",
      " [ 5.7292]\n",
      " [ 5.1884]\n",
      " [ 6.3557]\n",
      " [ 9.7687]\n",
      " [ 6.5159]\n",
      " [ 8.5172]\n",
      " [ 9.1802]\n",
      " [ 6.002 ]\n",
      " [ 5.5204]\n",
      " [ 5.0594]\n",
      " [ 5.7077]\n",
      " [ 7.6366]\n",
      " [ 5.8707]\n",
      " [ 5.3054]\n",
      " [ 8.2934]\n",
      " [13.394 ]\n",
      " [ 5.4369]] \n",
      " The shape of X =  (97, 1)  \n",
      " Output (Y) =  [17.592    9.1302  13.662   11.854    6.8233  11.886    4.3483  12.\n",
      "  6.5987   3.8166   3.2522  15.505    3.1551   7.2258   0.71618  3.5129\n",
      "  5.3048   0.56077  3.6518   5.3893   3.1386  21.767    4.263    5.1875\n",
      "  3.0825  22.638   13.501    7.0467  14.692   24.147   -1.22     5.9966\n",
      " 12.134    1.8495   6.5426   4.5623   4.1164   3.3928  10.117    5.4974\n",
      "  0.55657  3.9115   5.3854   2.4406   6.7318   1.0463   5.1337   1.844\n",
      "  8.0043   1.0179   6.7504   1.8396   4.2885   4.9981   1.4233  -1.4211\n",
      "  2.4756   4.6042   3.9624   5.4141   5.1694  -0.74279 17.929   12.054\n",
      " 17.054    4.8852   5.7442   7.7754   1.0173  20.992    6.6799   4.0259\n",
      "  1.2784   3.3411  -2.6807   0.29678  3.8845   5.7014   6.7526   2.0576\n",
      "  0.47953  0.20421  0.67861  7.5435   5.3436   4.2415   6.7981   0.92695\n",
      "  0.152    2.8214   1.8451   4.2959   7.2029   1.9869   0.14454  9.0551\n",
      "  0.61705] \n",
      " Shape of y =  (97,)\n",
      "Datatype of X and Y :  <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# Create the pandas DataFrame with the list and add column headers\n",
    "dataset= pd.DataFrame(data, columns = ['Population', 'Profit']) \n",
    "\n",
    "print(dataset)\n",
    "print('Datatype of X and Y : ', type(dataset))\n",
    "\n",
    "\n",
    "# Split the dataset into input (feature X) and output (Target y) from Pandas dataFrame named dataset\n",
    "\n",
    "X = dataset.iloc[:, :-1].values #All rows, all columns excluding the last column indexed -1\n",
    "y = dataset.iloc[:, 1].values #All rows, column 1\n",
    "\n",
    "print ('\\n Input Feature X = ', X, '\\n The shape of X = ',X.shape,  ' \\n Output (Y) = ', y, '\\n Shape of y = ',y.shape)\n",
    "print('Datatype of X and Y : ', type(X), type(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7020315537841397"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.89578087831185\n"
     ]
    }
   ],
   "source": [
    "print(regressor.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.19303364])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b7286c09b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the results\n",
    "plt.scatter(X, y, color = 'red') #Scatter plot of the X an y dataset\n",
    "\n",
    "plt.plot(X, regressor.predict(X), color = 'blue')#Line plot of X against predicted Y \n",
    "#(remember that output of regressor.predict is yhat)\n",
    "\n",
    "plt.title('Simple Linear Regression of Predictor vs Dependent variable')\n",
    "plt.xlabel('X (Predictor)')\n",
    "plt.ylabel('y (Dependent Variable)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict():\n",
    "    input_ = float(input('Enter the population size(10,000): '))\n",
    "    print(\"For a population of {}, the estimated profit is ${}\".format(input_,(input_ * 1.19303364) -3.89578087831185))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the population size(10,000): 6.1101\n",
      "For a population of 6.1101, the estimated profit is $3.3937739654521497\n"
     ]
    }
   ],
   "source": [
    "predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting the Dataset results on input feature alone\n",
    "\n",
    "y_pred = regressor.predict(X)\n",
    "#Note: store the result in y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Population</th>\n",
       "      <th>Profit</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.1101</td>\n",
       "      <td>17.5920</td>\n",
       "      <td>3.393774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.5277</td>\n",
       "      <td>9.1302</td>\n",
       "      <td>2.698951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.5186</td>\n",
       "      <td>13.6620</td>\n",
       "      <td>6.267196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0032</td>\n",
       "      <td>11.8540</td>\n",
       "      <td>4.459272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.8598</td>\n",
       "      <td>6.8233</td>\n",
       "      <td>3.095158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Population   Profit      yhat\n",
       "0      6.1101  17.5920  3.393774\n",
       "1      5.5277   9.1302  2.698951\n",
       "2      8.5186  13.6620  6.267196\n",
       "3      7.0032  11.8540  4.459272\n",
       "4      5.8598   6.8233  3.095158"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "   #Adding the result column to the existing dataset table\n",
    "\n",
    "dataset['yhat'] = y_pred\n",
    "\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b728a06828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#A bar plot showing the diffrence between actual value (y) and predicted value (yhat)\n",
    "dataset[['Profit','yhat']].plot(kind='bar',figsize=(16,10))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}