Supervised Learning/ML_Linear-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regression is used to find real values<br>\n",
    "It gives a general formula for the dataset<br>\n",
    "In this we calculate how much the difference in between the predicted value and the actual value for the whole dataset<br>\n",
    "Difference between actual value and predicted value is called cost function<br>\n",
    "Error is calculated for entire dataset using this cost function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To read CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# For importing algorithm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# For splitting between training and testing\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Importing accuracy score and mean_squared_error\n",
    "from sklearn.metrics import mean_squared_error, accuracy_score,mean_absolute_error\n",
    "# For plotting\n",
    "import matplotlib.pyplot as plt\n",
    "# For dimension reduction\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementing Simple Linear Regression for salary dataset<br>\n",
    "This dataset contains the years of experience and Salary of employees<br>\n",
    "First column dosen't have any significance in data (Only used for Index)<br>\n",
    "We can remove that<br>\n",
    "To remove that we have two options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Method 1-By giving usecols arguments and only using required columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    YearsExperience    Salary\n",
      "0               1.2   39344.0\n",
      "1               1.4   46206.0\n",
      "2               1.6   37732.0\n",
      "3               2.1   43526.0\n",
      "4               2.3   39892.0\n",
      "5               3.0   56643.0\n",
      "6               3.1   60151.0\n",
      "7               3.3   54446.0\n",
      "8               3.3   64446.0\n",
      "9               3.8   57190.0\n",
      "10              4.0   63219.0\n",
      "11              4.1   55795.0\n",
      "12              4.1   56958.0\n",
      "13              4.2   57082.0\n",
      "14              4.6   61112.0\n",
      "15              5.0   67939.0\n",
      "16              5.2   66030.0\n",
      "17              5.4   83089.0\n",
      "18              6.0   81364.0\n",
      "19              6.1   93941.0\n",
      "20              6.9   91739.0\n",
      "21              7.2   98274.0\n",
      "22              8.0  101303.0\n",
      "23              8.3  113813.0\n",
      "24              8.8  109432.0\n",
      "25              9.1  105583.0\n",
      "26              9.6  116970.0\n",
      "27              9.7  112636.0\n",
      "28             10.4  122392.0\n",
      "29             10.6  121873.0\n"
     ]
    }
   ],
   "source": [
    "data=pd.read_csv(\"Salary_dataset.csv\",usecols=['YearsExperience','Salary'])\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Method 2-By using the index_col and setting it to 0 so that it doesn't take index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    YearsExperience    Salary\n",
      "0               1.2   39344.0\n",
      "1               1.4   46206.0\n",
      "2               1.6   37732.0\n",
      "3               2.1   43526.0\n",
      "4               2.3   39892.0\n",
      "5               3.0   56643.0\n",
      "6               3.1   60151.0\n",
      "7               3.3   54446.0\n",
      "8               3.3   64446.0\n",
      "9               3.8   57190.0\n",
      "10              4.0   63219.0\n",
      "11              4.1   55795.0\n",
      "12              4.1   56958.0\n",
      "13              4.2   57082.0\n",
      "14              4.6   61112.0\n",
      "15              5.0   67939.0\n",
      "16              5.2   66030.0\n",
      "17              5.4   83089.0\n",
      "18              6.0   81364.0\n",
      "19              6.1   93941.0\n",
      "20              6.9   91739.0\n",
      "21              7.2   98274.0\n",
      "22              8.0  101303.0\n",
      "23              8.3  113813.0\n",
      "24              8.8  109432.0\n",
      "25              9.1  105583.0\n",
      "26              9.6  116970.0\n",
      "27              9.7  112636.0\n",
      "28             10.4  122392.0\n",
      "29             10.6  121873.0\n"
     ]
    }
   ],
   "source": [
    "data=pd.read_csv(\"Salary_dataset.csv\",index_col=0)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing<br>\n",
    "Splitting between Input and Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input is:\n",
      " [[ 1.2]\n",
      " [ 1.4]\n",
      " [ 1.6]\n",
      " [ 2.1]\n",
      " [ 2.3]\n",
      " [ 3. ]\n",
      " [ 3.1]\n",
      " [ 3.3]\n",
      " [ 3.3]\n",
      " [ 3.8]\n",
      " [ 4. ]\n",
      " [ 4.1]\n",
      " [ 4.1]\n",
      " [ 4.2]\n",
      " [ 4.6]\n",
      " [ 5. ]\n",
      " [ 5.2]\n",
      " [ 5.4]\n",
      " [ 6. ]\n",
      " [ 6.1]\n",
      " [ 6.9]\n",
      " [ 7.2]\n",
      " [ 8. ]\n",
      " [ 8.3]\n",
      " [ 8.8]\n",
      " [ 9.1]\n",
      " [ 9.6]\n",
      " [ 9.7]\n",
      " [10.4]\n",
      " [10.6]]\n",
      "\n",
      "Output is:\n",
      " [[ 39344.]\n",
      " [ 46206.]\n",
      " [ 37732.]\n",
      " [ 43526.]\n",
      " [ 39892.]\n",
      " [ 56643.]\n",
      " [ 60151.]\n",
      " [ 54446.]\n",
      " [ 64446.]\n",
      " [ 57190.]\n",
      " [ 63219.]\n",
      " [ 55795.]\n",
      " [ 56958.]\n",
      " [ 57082.]\n",
      " [ 61112.]\n",
      " [ 67939.]\n",
      " [ 66030.]\n",
      " [ 83089.]\n",
      " [ 81364.]\n",
      " [ 93941.]\n",
      " [ 91739.]\n",
      " [ 98274.]\n",
      " [101303.]\n",
      " [113813.]\n",
      " [109432.]\n",
      " [105583.]\n",
      " [116970.]\n",
      " [112636.]\n",
      " [122392.]\n",
      " [121873.]]\n",
      "Shape of Input data is: [[ 1.2]\n",
      " [ 1.4]\n",
      " [ 1.6]\n",
      " [ 2.1]\n",
      " [ 2.3]\n",
      " [ 3. ]\n",
      " [ 3.1]\n",
      " [ 3.3]\n",
      " [ 3.3]\n",
      " [ 3.8]\n",
      " [ 4. ]\n",
      " [ 4.1]\n",
      " [ 4.1]\n",
      " [ 4.2]\n",
      " [ 4.6]\n",
      " [ 5. ]\n",
      " [ 5.2]\n",
      " [ 5.4]\n",
      " [ 6. ]\n",
      " [ 6.1]\n",
      " [ 6.9]\n",
      " [ 7.2]\n",
      " [ 8. ]\n",
      " [ 8.3]\n",
      " [ 8.8]\n",
      " [ 9.1]\n",
      " [ 9.6]\n",
      " [ 9.7]\n",
      " [10.4]\n",
      " [10.6]] Shape of Output data is: [[ 39344.]\n",
      " [ 46206.]\n",
      " [ 37732.]\n",
      " [ 43526.]\n",
      " [ 39892.]\n",
      " [ 56643.]\n",
      " [ 60151.]\n",
      " [ 54446.]\n",
      " [ 64446.]\n",
      " [ 57190.]\n",
      " [ 63219.]\n",
      " [ 55795.]\n",
      " [ 56958.]\n",
      " [ 57082.]\n",
      " [ 61112.]\n",
      " [ 67939.]\n",
      " [ 66030.]\n",
      " [ 83089.]\n",
      " [ 81364.]\n",
      " [ 93941.]\n",
      " [ 91739.]\n",
      " [ 98274.]\n",
      " [101303.]\n",
      " [113813.]\n",
      " [109432.]\n",
      " [105583.]\n",
      " [116970.]\n",
      " [112636.]\n",
      " [122392.]\n",
      " [121873.]]\n"
     ]
    }
   ],
   "source": [
    "X,Y=data['YearsExperience'].to_numpy().reshape(-1,1),data['Salary'].to_numpy().reshape(-1,1)\n",
    "print(\"Input is:\\n\",X)\n",
    "print(\"\\nOutput is:\\n\",Y)\n",
    "print(\"Shape of Input data is:\",X,\"Shape of Output data is:\",Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting into training and testing data<br>\n",
    "we can do this directly as dataset is small instead of using train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data is:\n",
      " [[1.2]\n",
      " [1.4]\n",
      " [1.6]\n",
      " [2.1]\n",
      " [2.3]\n",
      " [3. ]\n",
      " [3.1]\n",
      " [3.3]\n",
      " [3.3]\n",
      " [3.8]\n",
      " [4. ]\n",
      " [4.1]\n",
      " [4.1]\n",
      " [4.2]\n",
      " [4.6]\n",
      " [5. ]\n",
      " [5.2]\n",
      " [5.4]\n",
      " [6. ]\n",
      " [6.1]\n",
      " [6.9]\n",
      " [7.2]\n",
      " [8. ]\n",
      " [8.3]\n",
      " [8.8]]\n",
      "\n",
      "Testing data is:\n",
      " [[ 39344.]\n",
      " [ 46206.]\n",
      " [ 37732.]\n",
      " [ 43526.]\n",
      " [ 39892.]\n",
      " [ 56643.]\n",
      " [ 60151.]\n",
      " [ 54446.]\n",
      " [ 64446.]\n",
      " [ 57190.]\n",
      " [ 63219.]\n",
      " [ 55795.]\n",
      " [ 56958.]\n",
      " [ 57082.]\n",
      " [ 61112.]\n",
      " [ 67939.]\n",
      " [ 66030.]\n",
      " [ 83089.]\n",
      " [ 81364.]\n",
      " [ 93941.]\n",
      " [ 91739.]\n",
      " [ 98274.]\n",
      " [101303.]\n",
      " [113813.]\n",
      " [109432.]]\n",
      "Shape of Training data is: (25, 1) Shape of Testing data is: (25, 1)\n"
     ]
    }
   ],
   "source": [
    "x_train=X[:-5]\n",
    "x_test=X[-5:]\n",
    "y_train=Y[:-5]\n",
    "y_test=Y[-5:]\n",
    "print(\"Training data is:\\n\",x_train)\n",
    "print(\"\\nTesting data is:\\n\",y_train)\n",
    "print(\"Shape of Training data is:\",x_train.shape,\"Shape of Testing data is:\",y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choosing algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg=LinearRegression()\n",
    "# Training data\n",
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy score is: 92.98889340527403\n",
      "Predicted values are:\n",
      "\n",
      "[[113773.84320812]\n",
      " [118768.07936621]\n",
      " [119766.92659783]\n",
      " [126758.85721916]\n",
      " [128756.55168239]]\n",
      "Predicted value for 1.5 is:\n",
      " [[37861.45360513]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Accuracy score is:\",reg.score(x_train,y_train)*100)\n",
    "# Checking predicted values\n",
    "predict=reg.predict(x_test)\n",
    "print(\"Predicted values are:\\n\")\n",
    "print(predict)\n",
    "# Checking predicted value for given value\n",
    "print(\"Predicted value for 1.5 is:\\n\",reg.predict([[1.5]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "print(accuracy_score(predict,y_test))-Doesn't work as accuracy score doesn't take array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared error is: 37525168.34948919\n",
      "Mean Absolute error is: 5674.051614740992\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Squared error is:\",mean_squared_error(y_test,predict))\n",
    "print(\"Mean Absolute error is:\",mean_absolute_error(y_test,predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting<br>\n",
    "We can see the points are much away from the line<br>\n",
    "Cost function is very high<br>\n",
    "This is why absolute and mean squared error is so high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "plt.scatter(x_test,y_test,color=\"blue\")\n",
    "plt.plot(x_test,predict,color=\"red\")\n",
    "plt.title(\"Regression Plot of Years of Experience VS Salary\")\n",
    "plt.xlabel(\"Years of Experience\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.legend(['Actual Values','Best Line or General formula'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementing Simple Linear Regression for housing dataset<br>\n",
    "Trying for USA housing dataset<br>\n",
    "area population(input) and price(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we need only two columns we will only import this two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset is:\n",
      "       Area Population         Price\n",
      "0        23086.800503  1.059034e+06\n",
      "1        40173.072174  1.505891e+06\n",
      "2        36882.159400  1.058988e+06\n",
      "3        34310.242831  1.260617e+06\n",
      "4        26354.109472  6.309435e+05\n",
      "...               ...           ...\n",
      "4995     22837.361035  1.060194e+06\n",
      "4996     25616.115489  1.482618e+06\n",
      "4997     33266.145490  1.030730e+06\n",
      "4998     42625.620156  1.198657e+06\n",
      "4999     46501.283803  1.298950e+06\n",
      "\n",
      "[5000 rows x 2 columns]\n",
      "\n",
      "Columns are:\n",
      " Index(['Area Population', 'Price'], dtype='object')\n",
      "Dimension of Area Population column before reshaping to array is: (5000,)\n",
      "Dimension of Price column before reshaping to array is: (5000,)\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv(\"USA_Housing.csv\",usecols=['Area Population','Price'])\n",
    "print(\"Dataset is:\\n\",df)\n",
    "print(\"\\nColumns are:\\n\",df.columns)\n",
    "# Seeing dimension of column before reshaping\n",
    "print(\"Dimension of Area Population column before reshaping to array is:\",df['Area Population'].shape)\n",
    "print(\"Dimension of Price column before reshaping to array is:\",df['Price'].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing<br>\n",
    "Splitting between Input and Output<br>\n",
    "Columns are in dataframe ,thus they are in series format so we need to convert them to array and then also reshape them so that algorithm can use them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input is:\n",
      " [[23086.80050269]\n",
      " [40173.07217364]\n",
      " [36882.1593997 ]\n",
      " ...\n",
      " [33266.14548965]\n",
      " [42625.62015585]\n",
      " [46501.28380314]]\n",
      "\n",
      "Output is:\n",
      " [[1059033.55787012]\n",
      " [1505890.91484695]\n",
      " [1058987.98787608]\n",
      " ...\n",
      " [1030729.58315229]\n",
      " [1198656.87240769]\n",
      " [1298950.48026696]]\n",
      "Shape of Input data(Area Population) is: (5000, 1) Shape of Output data(Price) is: (5000, 1)\n"
     ]
    }
   ],
   "source": [
    "X,Y=df['Area Population'].to_numpy().reshape(-1,1),df['Price'].to_numpy().reshape(-1,1)\n",
    "print(\"Input is:\\n\",X)\n",
    "print(\"\\nOutput is:\\n\",Y)\n",
    "# Seeing dimension of column after reshaping\n",
    "print(\"Shape of Input data(Area Population) is:\",X.shape,\"Shape of Output data(Price) is:\",Y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting into training and testing data using train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data is:\n",
      " [[46462.31347828]\n",
      " [38725.42430316]\n",
      " [19332.41977602]\n",
      " ...\n",
      " [17981.45046533]\n",
      " [35008.26751377]\n",
      " [19481.38512536]]\n",
      "\n",
      "Testing data is:\n",
      " [[1510883.77735139]\n",
      " [ 696014.46771489]\n",
      " [1267150.83668714]\n",
      " ...\n",
      " [ 699222.83614379]\n",
      " [1368134.99768214]\n",
      " [1075550.21547577]]\n",
      "Shape of Training data is: (4000, 1) Shape of Testing data is: (4000, 1)\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(X,Y,train_size=0.8)\n",
    "print(\"Training data is:\\n\",x_train)\n",
    "print(\"\\nTesting data is:\\n\",y_train)\n",
    "print(\"Shape of Training data is:\",x_train.shape,\"Shape of Testing data is:\",y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choosing algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg=LinearRegression()\n",
    "# Training data\n",
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating model<br>\n",
    "Since the dataset is very big and we are only using one column accuracy is less"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy score is: 15.990908211624344\n",
      "Predicted values are:\n",
      "\n",
      "[[1293610.82374211]\n",
      " [1207803.39154316]\n",
      " [1225123.8600657 ]\n",
      " [1216517.96277612]\n",
      " [1021641.67337924]\n",
      " [1189088.56065548]\n",
      " [1057049.59032821]\n",
      " [1193092.9960161 ]\n",
      " [1294970.97614272]\n",
      " [1236594.70103799]\n",
      " [1258731.53614207]\n",
      " [1214908.72587491]\n",
      " [1486151.25632455]\n",
      " [1192460.7515734 ]\n",
      " [1233738.53802919]\n",
      " [1361888.93505293]\n",
      " [1108353.92896693]\n",
      " [1184785.22296736]\n",
      " [1382044.31822395]\n",
      " [1021135.58399278]\n",
      " [1193133.56420526]\n",
      " [1442052.76031093]\n",
      " [1079216.70790219]\n",
      " [1358791.51265357]\n",
      " [1048388.93907745]\n",
      " [1307433.07654886]\n",
      " [1405614.88233272]\n",
      " [1106710.31024429]\n",
      " [1112760.47210712]\n",
      " [1299923.1473889 ]\n",
      " [1380167.16791927]\n",
      " [1419601.79367626]\n",
      " [1048858.66759709]\n",
      " [1346438.29416906]\n",
      " [1354519.93734749]\n",
      " [1417788.19837074]\n",
      " [1100092.64953032]\n",
      " [1386759.3209248 ]\n",
      " [1291900.02794427]\n",
      " [1376401.94546229]\n",
      " [1447332.55747717]\n",
      " [ 878780.52610903]\n",
      " [1348616.71703458]\n",
      " [1195412.36295123]\n",
      " [1449446.56150268]\n",
      " [1096861.34802392]\n",
      " [1178905.60583571]\n",
      " [1443718.5920148 ]\n",
      " [1268898.55043553]\n",
      " [1040240.03206032]\n",
      " [1336966.07523814]\n",
      " [1021325.90010159]\n",
      " [1429165.30010666]\n",
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      " [1192125.09615594]\n",
      " [1024368.92347395]\n",
      " [1197097.7111025 ]\n",
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      " [1403750.74236488]\n",
      " [ 992257.349677  ]\n",
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      " [1120515.1832308 ]\n",
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      " [1286085.0528236 ]\n",
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      " [1381869.0118396 ]\n",
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      " [1231762.1523299 ]\n",
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      " [ 938952.11180562]\n",
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      " [1012112.6411297 ]\n",
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      " [1133322.3698648 ]\n",
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      " [1423945.05992664]\n",
      " [1406399.1670124 ]\n",
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      " [1352060.38895916]\n",
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      " [1293526.16590854]\n",
      " [1218897.73272637]\n",
      " [1288501.07069208]\n",
      " [1324803.25604077]\n",
      " [1334309.47224929]\n",
      " [ 988460.17696988]\n",
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      " [1332722.42006465]\n",
      " [1194955.7887884 ]\n",
      " [1292574.23718834]\n",
      " [1122199.2201101 ]\n",
      " [1604613.56515837]\n",
      " [1381411.48972844]\n",
      " [1061354.55055642]\n",
      " [1203107.38331482]\n",
      " [1407687.48544657]\n",
      " [1114045.21980654]\n",
      " [1202983.8894735 ]\n",
      " [1344352.83393332]\n",
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      " [1129832.28939712]\n",
      " [1276497.56023579]\n",
      " [1080631.4123849 ]\n",
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      " [1160892.44406605]\n",
      " [1511698.91380487]\n",
      " [ 936238.85675615]\n",
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      " [1125989.93255692]\n",
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      " [1113424.59982061]\n",
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      " [1217216.67766503]\n",
      " [1391563.06611108]\n",
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      " [1280696.13882903]\n",
      " [1082113.25133638]\n",
      " [1414144.22550542]\n",
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      " [1213407.48548066]\n",
      " [1462258.07634701]\n",
      " [1470965.8915989 ]\n",
      " [1320956.43975332]\n",
      " [ 997302.20196239]\n",
      " [1376139.45176681]\n",
      " [1018106.0318902 ]\n",
      " [1139222.78111666]\n",
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      " [1080502.31339004]\n",
      " [1403405.11225701]\n",
      " [1137757.22750065]\n",
      " [1216339.2926637 ]\n",
      " [1099216.73098807]\n",
      " [1140756.73334291]\n",
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      " [1306280.63497578]\n",
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      " [ 991626.52671324]\n",
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      " [1183073.81114982]\n",
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      " [1468269.11589894]\n",
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      " [1411232.48803497]\n",
      " [1216123.0061169 ]\n",
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      " [1182484.2964507 ]\n",
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      " [ 934330.40960304]\n",
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      " [1279676.4136811 ]\n",
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      " [1409700.07344383]\n",
      " [1443466.55874885]\n",
      " [1366971.90816227]\n",
      " [1028872.28467151]\n",
      " [1258982.06739802]\n",
      " [ 944051.32066871]\n",
      " [1349253.45298956]\n",
      " [1384706.25311125]\n",
      " [ 911708.83227848]\n",
      " [1149558.47585528]\n",
      " [1031741.5981171 ]\n",
      " [1329460.36764279]\n",
      " [1187673.19629396]\n",
      " [1240266.55675692]\n",
      " [1170904.9237663 ]\n",
      " [1235509.32188459]\n",
      " [1150923.89913501]\n",
      " [1374198.65308908]\n",
      " [1047092.86400928]\n",
      " [1407713.50423024]\n",
      " [1231650.43596821]\n",
      " [1339441.28741508]\n",
      " [1334128.14848354]\n",
      " [1240543.3120562 ]\n",
      " [1324832.59739936]\n",
      " [1288179.85122052]\n",
      " [1036369.50517622]\n",
      " [1366800.93652058]\n",
      " [1559905.35420244]\n",
      " [1388549.17091849]\n",
      " [ 932318.13719768]\n",
      " [1136228.40014601]\n",
      " [1226007.78298275]\n",
      " [ 972360.13244062]\n",
      " [1135063.93429241]\n",
      " [1117479.82817644]\n",
      " [1388124.4133251 ]\n",
      " [1259393.83193949]\n",
      " [1142259.54836394]\n",
      " [1077770.24432942]\n",
      " [1290498.88222066]\n",
      " [1317165.22398313]\n",
      " [1563068.70248894]\n",
      " [1126957.37593   ]\n",
      " [1211106.015889  ]\n",
      " [1044725.9978534 ]\n",
      " [1171681.73697736]\n",
      " [1038479.74561544]\n",
      " [1226927.88743142]\n",
      " [1133722.80330207]\n",
      " [1414672.04967963]\n",
      " [1141860.7282735 ]\n",
      " [1265992.2053506 ]\n",
      " [ 946390.09828564]\n",
      " [1264753.16875577]\n",
      " [1246009.59513898]\n",
      " [1177065.81844001]\n",
      " [1300014.53314363]\n",
      " [1227457.09177169]\n",
      " [1009067.10455724]\n",
      " [1314129.61574233]\n",
      " [1447596.4015413 ]\n",
      " [1484613.12687503]\n",
      " [1487953.73762868]\n",
      " [1323992.7425492 ]\n",
      " [1046385.93018582]\n",
      " [1348576.38596342]\n",
      " [1194609.13514605]\n",
      " [1576916.11977537]\n",
      " [1288123.95517057]\n",
      " [1163529.37209919]\n",
      " [1166899.1335464 ]\n",
      " [1300813.68401746]\n",
      " [1043451.1742652 ]\n",
      " [ 818643.35866908]\n",
      " [1380966.95585863]\n",
      " [1156269.99315882]\n",
      " [1121677.09043872]\n",
      " [1224253.13940619]\n",
      " [1228526.11988476]\n",
      " [1010500.12811671]\n",
      " [1058529.46578006]\n",
      " [1246151.13834121]\n",
      " [1263870.74969901]\n",
      " [1128049.62962569]\n",
      " [1212670.68515452]\n",
      " [1315361.69711454]\n",
      " [ 959110.29668397]\n",
      " [1121524.42255626]\n",
      " [1374864.77916623]\n",
      " [1097618.30403652]\n",
      " [1134483.76232222]\n",
      " [1383480.45135271]\n",
      " [1045365.03966786]\n",
      " [1517799.8756201 ]\n",
      " [1077955.61001524]\n",
      " [1178553.35722019]\n",
      " [1376403.15441978]\n",
      " [1142794.70727536]\n",
      " [ 924486.60825562]\n",
      " [1361368.57756282]\n",
      " [1090939.14836145]\n",
      " [1295923.83782236]\n",
      " [1152476.86776902]\n",
      " [1493926.43299205]\n",
      " [1371065.7538576 ]\n",
      " [1105998.49628749]\n",
      " [1432192.81857606]\n",
      " [1236105.88422814]\n",
      " [1136248.57333607]\n",
      " [1237510.36663607]\n",
      " [1325951.52897586]\n",
      " [1512215.76225424]\n",
      " [1184508.74729805]\n",
      " [1163570.17260761]\n",
      " [1304251.09620919]\n",
      " [1210175.5406351 ]\n",
      " [ 911636.47451949]\n",
      " [1044266.79184134]\n",
      " [1205091.64688108]\n",
      " [1057389.69278879]\n",
      " [1320794.01722835]\n",
      " [1260769.93390151]\n",
      " [1236935.16666623]\n",
      " [1393611.7523306 ]\n",
      " [1310052.25569126]\n",
      " [1187253.63578108]\n",
      " [1104240.06361641]\n",
      " [1203174.97478663]\n",
      " [1362374.36914914]\n",
      " [1231833.96270143]]\n",
      "Predicted value for 1.5 is:\n",
      " [[721343.24170818]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Accuracy score is:\",reg.score(x_train,y_train)*100)\n",
    "# Checking predicted values\n",
    "predict=reg.predict(x_test)\n",
    "print(\"Predicted values are:\\n\")\n",
    "print(predict)\n",
    "# Checking predicted value for given value\n",
    "print(\"Predicted value for 1.5 is:\\n\",reg.predict([[1.5]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "print(accuracy_score(predict,y_test))-Doesn't work as accuracy score dosen't take array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for Error<br>\n",
    "Error is more as accuracy is less"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared error is: 101526077079.22557\n",
      "Mean Absolute error is: 254552.980356407\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Squared error is:\",mean_squared_error(y_test,predict))\n",
    "print(\"Mean Absolute error is:\",mean_absolute_error(y_test,predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting<br>\n",
    "We can see the points are much away from the line<br>\n",
    "Cost function is very high<br>\n",
    "This is why absolute and mean squared error is so high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.scatter(x_test,y_test,color=\"blue\")\n",
    "plt.plot(x_test,predict,color=\"red\")\n",
    "plt.legend(['Actual Values','Best Line or General formula'])\n",
    "plt.title(\"Regression Plot of Area Population VS Price\")\n",
    "plt.xlabel(\"Area Population\")\n",
    "plt.ylabel(\"Price\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementing Multiple Linear Regression for housing dataset<br>\n",
    "Trying for USA housing dataset<br>\n",
    "area population and all parameters(input) and price(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset is:\n",
      "       Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
      "0         79545.458574             5.682861                   7.009188   \n",
      "1         79248.642455             6.002900                   6.730821   \n",
      "2         61287.067179             5.865890                   8.512727   \n",
      "3         63345.240046             7.188236                   5.586729   \n",
      "4         59982.197226             5.040555                   7.839388   \n",
      "...                ...                  ...                        ...   \n",
      "4995      60567.944140             7.830362                   6.137356   \n",
      "4996      78491.275435             6.999135                   6.576763   \n",
      "4997      63390.686886             7.250591                   4.805081   \n",
      "4998      68001.331235             5.534388                   7.130144   \n",
      "4999      65510.581804             5.992305                   6.792336   \n",
      "\n",
      "      Avg. Area Number of Bedrooms  Area Population         Price  \\\n",
      "0                             4.09     23086.800503  1.059034e+06   \n",
      "1                             3.09     40173.072174  1.505891e+06   \n",
      "2                             5.13     36882.159400  1.058988e+06   \n",
      "3                             3.26     34310.242831  1.260617e+06   \n",
      "4                             4.23     26354.109472  6.309435e+05   \n",
      "...                            ...              ...           ...   \n",
      "4995                          3.46     22837.361035  1.060194e+06   \n",
      "4996                          4.02     25616.115489  1.482618e+06   \n",
      "4997                          2.13     33266.145490  1.030730e+06   \n",
      "4998                          5.44     42625.620156  1.198657e+06   \n",
      "4999                          4.07     46501.283803  1.298950e+06   \n",
      "\n",
      "                                                Address  \n",
      "0     208 Michael Ferry Apt. 674\\nLaurabury, NE 3701...  \n",
      "1     188 Johnson Views Suite 079\\nLake Kathleen, CA...  \n",
      "2     9127 Elizabeth Stravenue\\nDanieltown, WI 06482...  \n",
      "3                             USS Barnett\\nFPO AP 44820  \n",
      "4                            USNS Raymond\\nFPO AE 09386  \n",
      "...                                                 ...  \n",
      "4995                   USNS Williams\\nFPO AP 30153-7653  \n",
      "4996              PSC 9258, Box 8489\\nAPO AA 42991-3352  \n",
      "4997  4215 Tracy Garden Suite 076\\nJoshualand, VA 01...  \n",
      "4998                          USS Wallace\\nFPO AE 73316  \n",
      "4999  37778 George Ridges Apt. 509\\nEast Holly, NV 2...  \n",
      "\n",
      "[5000 rows x 7 columns]\n",
      "\n",
      "Columns are:\n",
      " Index(['Avg. Area Income', 'Avg. Area House Age', 'Avg. Area Number of Rooms',\n",
      "       'Avg. Area Number of Bedrooms', 'Area Population', 'Price', 'Address'],\n",
      "      dtype='object')\n",
      "Dimension of Area Population column before reshaping to array is: (5000,)\n",
      "Dimension of Price column before reshaping to array is: (5000,)\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv(\"USA_Housing.csv\")\n",
    "print(\"Dataset is:\\n\",df)\n",
    "print(\"\\nColumns are:\\n\",df.columns)\n",
    "# This column is not useful so we drop it\n",
    "df=df.drop('Address',axis=1)\n",
    "# Seeing dimension of column before reshaping\n",
    "print(\"Dimension of Area Population column before reshaping to array is:\",df['Area Population'].shape)\n",
    "print(\"Dimension of Price column before reshaping to array is:\",df['Price'].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Columns are in dataframe ,thus they are in series format so we need to convert them to array and then also reshape them so that algorithm can use them<br>\n",
    "Function to reshape columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset after reshaping is:\n",
      "       Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
      "0         79545.458574             5.682861                   7.009188   \n",
      "1         79248.642455             6.002900                   6.730821   \n",
      "2         61287.067179             5.865890                   8.512727   \n",
      "3         63345.240046             7.188236                   5.586729   \n",
      "4         59982.197226             5.040555                   7.839388   \n",
      "...                ...                  ...                        ...   \n",
      "4995      60567.944140             7.830362                   6.137356   \n",
      "4996      78491.275435             6.999135                   6.576763   \n",
      "4997      63390.686886             7.250591                   4.805081   \n",
      "4998      68001.331235             5.534388                   7.130144   \n",
      "4999      65510.581804             5.992305                   6.792336   \n",
      "\n",
      "      Avg. Area Number of Bedrooms  Area Population         Price  \n",
      "0                             4.09     23086.800503  1.059034e+06  \n",
      "1                             3.09     40173.072174  1.505891e+06  \n",
      "2                             5.13     36882.159400  1.058988e+06  \n",
      "3                             3.26     34310.242831  1.260617e+06  \n",
      "4                             4.23     26354.109472  6.309435e+05  \n",
      "...                            ...              ...           ...  \n",
      "4995                          3.46     22837.361035  1.060194e+06  \n",
      "4996                          4.02     25616.115489  1.482618e+06  \n",
      "4997                          2.13     33266.145490  1.030730e+06  \n",
      "4998                          5.44     42625.620156  1.198657e+06  \n",
      "4999                          4.07     46501.283803  1.298950e+06  \n",
      "\n",
      "[5000 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "func = lambda i:df[i].to_numpy().reshape(-1,1)\n",
    "for i in df.columns:\n",
    "    df[i]=func(i)\n",
    "print(\"Dataset after reshaping is:\\n\",df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seeing dimension of column after reshaping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimension of Area Population column after reshaping to array is: (5000,)\n",
      "Dimension of Price column after reshaping to array is: (5000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Dimension of Area Population column after reshaping to array is:\",df['Area Population'].shape)\n",
    "print(\"Dimension of Price column after reshaping to array is:\",df['Price'].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing<br>\n",
    "Splitting between Input and Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input is:\n",
      "       Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
      "0         79545.458574             5.682861                   7.009188   \n",
      "1         79248.642455             6.002900                   6.730821   \n",
      "2         61287.067179             5.865890                   8.512727   \n",
      "3         63345.240046             7.188236                   5.586729   \n",
      "4         59982.197226             5.040555                   7.839388   \n",
      "...                ...                  ...                        ...   \n",
      "4995      60567.944140             7.830362                   6.137356   \n",
      "4996      78491.275435             6.999135                   6.576763   \n",
      "4997      63390.686886             7.250591                   4.805081   \n",
      "4998      68001.331235             5.534388                   7.130144   \n",
      "4999      65510.581804             5.992305                   6.792336   \n",
      "\n",
      "      Avg. Area Number of Bedrooms  Area Population  \n",
      "0                             4.09     23086.800503  \n",
      "1                             3.09     40173.072174  \n",
      "2                             5.13     36882.159400  \n",
      "3                             3.26     34310.242831  \n",
      "4                             4.23     26354.109472  \n",
      "...                            ...              ...  \n",
      "4995                          3.46     22837.361035  \n",
      "4996                          4.02     25616.115489  \n",
      "4997                          2.13     33266.145490  \n",
      "4998                          5.44     42625.620156  \n",
      "4999                          4.07     46501.283803  \n",
      "\n",
      "[5000 rows x 5 columns]\n",
      "\n",
      "Output is:\n",
      " 0       1.059034e+06\n",
      "1       1.505891e+06\n",
      "2       1.058988e+06\n",
      "3       1.260617e+06\n",
      "4       6.309435e+05\n",
      "            ...     \n",
      "4995    1.060194e+06\n",
      "4996    1.482618e+06\n",
      "4997    1.030730e+06\n",
      "4998    1.198657e+06\n",
      "4999    1.298950e+06\n",
      "Name: Price, Length: 5000, dtype: float64\n",
      "\n",
      "Shape of Input data is: (5000, 5) Shape of Output data is: (5000,)\n"
     ]
    }
   ],
   "source": [
    "X,Y=df.drop('Price',axis=1),df['Price']\n",
    "print(\"Input is:\\n\",X)\n",
    "print(\"\\nOutput is:\\n\",Y)\n",
    "print(\"\\nShape of Input data is:\",X.shape,\"Shape of Output data is:\",Y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting into training and testing data using train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data is:\n",
      "       Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
      "4152      74264.417479             5.556855                   7.143696   \n",
      "1323      80233.228301             5.334072                   7.034766   \n",
      "1098      72469.541778             5.606395                   8.044576   \n",
      "4076      66039.622184             4.568032                   5.243971   \n",
      "436       75070.262571             6.680451                   6.261276   \n",
      "...                ...                  ...                        ...   \n",
      "2750      44218.541110             5.589396                   9.449250   \n",
      "2197      68057.065802             6.748409                   7.883019   \n",
      "1067      41533.012960             6.853578                   5.055629   \n",
      "4989      47965.406896             5.694638                   7.363327   \n",
      "3674      77449.316068             5.034661                   6.760959   \n",
      "\n",
      "      Avg. Area Number of Bedrooms  Area Population  \n",
      "4152                          3.31     47367.072260  \n",
      "1323                          5.30     26263.054963  \n",
      "1098                          3.07     27227.813387  \n",
      "4076                          4.50     28616.050685  \n",
      "436                           2.29     44682.757332  \n",
      "...                            ...              ...  \n",
      "2750                          4.23     35420.740943  \n",
      "2197                          6.20     52547.829166  \n",
      "1067                          4.24     31685.402692  \n",
      "4989                          5.40     46071.947337  \n",
      "3674                          2.02     30054.786866  \n",
      "\n",
      "[4000 rows x 5 columns]\n",
      "\n",
      "Testing data is:\n",
      " 4152    1.379671e+06\n",
      "1323    1.390498e+06\n",
      "1098    1.123387e+06\n",
      "4076    7.523573e+05\n",
      "436     1.681316e+06\n",
      "            ...     \n",
      "2750    9.205407e+05\n",
      "2197    1.754576e+06\n",
      "1067    6.822003e+05\n",
      "4989    8.852050e+05\n",
      "3674    1.161458e+06\n",
      "Name: Price, Length: 4000, dtype: float64\n",
      "Shape of Training data is: (4000, 5) Shape of Testing data is: (4000,)\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(X,Y,train_size=0.8)\n",
    "print(\"Training data is:\\n\",x_train)\n",
    "print(\"\\nTesting data is:\\n\",y_train)\n",
    "print(\"Shape of Training data is:\",x_train.shape,\"Shape of Testing data is:\",y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choosing algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg=LinearRegression()\n",
    "# Training data\n",
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating model<br>\n",
    "Since the dataset is very big and we are using all columns accuracy increases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy score is: 91.78285261348096\n",
      "Predicted values are:\n",
      "\n",
      "[1213054.44103987 1215273.13287561 1076829.13696745 1359938.26565803\n",
      " 1285833.59849402 1119630.98969266 1526426.42845774 1105440.59854579\n",
      " 1661399.51379782 1729158.26912196 1074865.00251733  993331.08870526\n",
      " 1627893.47099729  850072.72858009 1028684.30726434  823618.45476796\n",
      "  879846.83197769 1351604.85412508 1242224.22193302 1209779.48523967\n",
      "  791752.53291467 1767300.90942338  884883.99464402 1300127.34514708\n",
      "  675612.40185441 1671997.45260931 1411822.79458784 1296773.64412425\n",
      " 1154166.68913021 1653767.30630497 1106098.71343305  888059.06811775\n",
      " 1330731.19327721 1399190.16242318  999543.27550096 1454874.74840434\n",
      " 1436686.02211771 1306673.73749843 1632737.41694966  915493.76871232\n",
      "  984464.02857948 1303358.75190016 1263050.41530748 1412265.68278714\n",
      " 1276645.49766296  889163.37074684 1308939.11569748 1582510.20847508\n",
      "  844406.5262884  1462396.09393811  989029.82807772 1180793.09250321\n",
      " 1266147.44444483 1186934.39828485 1159935.67060036 1434066.50161014\n",
      " 1469869.56850208 1289695.65323201 1314281.75754725 1293509.1378188\n",
      " 1124833.12256363  879600.29536087  673910.12390757 1188656.61875592\n",
      "  843925.53718947 1561015.20558128 1736155.18088058 1093025.07811009\n",
      "  970081.60814825 1198478.10966673 1965474.70074298  929815.8929959\n",
      " 1347683.32394725 1506992.2670259   528190.65737054 1437809.57992853\n",
      " 1791365.59113282 1781976.68778266 1573572.05321435  952012.90612395\n",
      " 1447900.18232296  956485.01329143 1764149.67613216 1300893.78612064\n",
      "  790362.64573143 1412631.48134333 1197236.46335793 1428217.08932288\n",
      " 1322436.44407759 1062975.10356321 1032604.79907521 1822537.80406412\n",
      " 1717084.09401136 1132459.08104398 1798825.27949738 1119259.36204078\n",
      " 1029847.11994993 1185886.40130139 1571635.75667351 1611515.87512734\n",
      " 1335195.25940869 1428105.23136213 1126125.10003652 1092554.0451351\n",
      " 1391751.61218696 1135284.28391218 1612310.61612545 1382219.45207523\n",
      " 1062660.14652885  993876.16926913 1682459.43202428  643362.54786734\n",
      " 1556538.5934791   699441.28375483 1570116.89504809 1008066.92996509\n",
      " 1477931.20211117 1587236.21644883  857853.20659143  776049.88911468\n",
      " 1051575.88439068 1535386.89528037 1483480.72530954 1019173.63333229\n",
      "  987471.45409196 1196730.64788322 1097184.91405575 1166374.04351719\n",
      " 1859486.27590959 1367540.74154551 1069085.8215671  1492917.46230278\n",
      " 1545593.42990741 1524159.45130317 1051432.01201173 1066763.9971053\n",
      " 1507699.93104511 1767339.47674807 1807340.56970717 1713069.76427253\n",
      " 1283287.77762042  916837.52086375 1632985.4127537  1447879.45536364\n",
      " 1415151.99553615 1321600.60917723  804834.68919738 1179959.5839025\n",
      " 1218795.40752707 1051673.62220081 1523635.57206959  701306.95767082\n",
      " 1329590.71860287  763627.4892824  1561727.00780413 1457973.70095389\n",
      " 1086694.89397186 1282846.08694053 1425368.89591365  375201.71369709\n",
      "  855239.73966729  908830.04339339 1224266.90391431 1424498.13056073\n",
      " 1455870.92525938  886514.77404996 1540828.69079453 1171985.62527435\n",
      "  804139.67266291 1623248.33674195 1212835.04848296 1765423.90132517\n",
      " 1770772.09731146 1791582.68623666  930936.71224724 1573559.07816269\n",
      "  967382.06441086  721277.490387   1948453.85058726  744094.69963364\n",
      " 1071788.08671612  856813.26068449 1076552.98225529  974579.03401196\n",
      " 1824812.36508723 1883864.12523908 1233519.70694907 1013124.38394022\n",
      "  866510.16844908 1245454.23629737  988015.3121598  1286045.74148653\n",
      " 1094076.79116438 1269097.22446    1502303.71943419 1270340.30978756\n",
      " 1296158.83840709 1050116.28400997 1197961.17973279  706005.82507643\n",
      " 1120490.52900919 1232259.79059377 1725556.9491616  1385173.0221878\n",
      " 1685514.75381958 1463056.07608878  968118.14170141 1284086.22506038\n",
      " 1428845.78402792  570760.93575706 1078755.03996706 1442680.56626109\n",
      "  867336.15514246 1123203.06440361  919737.77210956 1038652.37347009\n",
      " 1472180.41067885 1280967.68164266  879590.68627031  784518.03201821\n",
      "  890618.61710768 1421225.84967651 1098671.38476157 1139215.98874461\n",
      " 1784511.48392386 1104852.44094427  756134.54205478 1653561.23146629\n",
      " 1278606.10390538 1474686.81738611 1410112.11642273 1542757.74200054\n",
      " 1848910.89197931 1025350.23453118 1231617.25638288  796671.3108383\n",
      " 1098733.16252266 1191425.80153154 1820878.68586227 1706519.54727541\n",
      " 1425643.1732065  1130884.69717641 2008839.18835651 1130756.22003496\n",
      "  446165.50084141 1433367.64393974  375742.21139851 1233385.38328786\n",
      " 1536538.91942578 1208570.9702323   898490.85001537 1445753.83609825\n",
      " 1450252.81851783 1404956.73502403  490760.64723122 1291436.96345086\n",
      " 1694553.46435212 1772332.82956824 1765470.61174991 1512848.18979359\n",
      " 1735377.25109295  949554.43886048 1799595.24370135 1345230.81781747\n",
      " 1356488.93749002 1153941.48970061  914135.74702323  985871.92631667\n",
      " 1131099.21666957  576800.6429326  1564536.1114655  1647420.52537898\n",
      "  891230.24522389  628421.46947735 1707445.01722141 1281068.20564562\n",
      "  661982.43872661 1310917.05530671 1182096.668141   1271125.96586478\n",
      "  887806.49791447 1059371.69013385 1259008.92823355 1038199.65353639\n",
      " 1327329.28605852 1657270.62483141 1396633.48317876 1493453.45942413\n",
      "  760600.08044537 1010687.16132638 1761194.63222095 1433433.94670408\n",
      "  668649.90377884 1507935.02822921  591394.93413596  797005.66724755\n",
      " 1324232.42994128 1255428.89835002 1234502.28369539 1255159.29489615\n",
      " 1003300.02738888  889015.17383658 1099181.54772457 1339248.99851736\n",
      " 1508566.83510387 1470162.11677074 1263659.26508343  778918.45015371\n",
      "  549488.28487801 1211300.27201442 1188899.79965837  698097.60233075\n",
      " 1102476.01643691 1509114.56996475  919825.93847101 1064027.82489256\n",
      " 1695679.69147294  977289.63804664  952115.28723326  539225.96343258\n",
      " 1429231.68093077  351967.91203882  910565.40035505 1529080.74720553\n",
      " 1146968.95780059 1440911.39006887 1777256.36750286 1327663.71250494\n",
      " 1676896.87676089 1629951.67971772 1134350.32979821 1183781.40291754\n",
      " 1331624.60021219 1519045.65734411 1130558.50151837 1277982.12722085\n",
      " 1522097.38943175 1251738.51741073 1453247.13968603  919155.95188597\n",
      " 1385638.50716673 1549761.15749945 1393931.45278874 1630055.56252949\n",
      "  869342.64357395  676011.73592714 1306085.24420827 1499803.78842735\n",
      "  505621.7233829  1103458.02295416 1116521.85488603  977696.45109961\n",
      " 1194519.40346216  987170.34036353 1750383.62672673 1389632.97280316\n",
      "  392059.7078099  1794792.51493324  742124.02579292 1658528.53163432\n",
      "  842003.87628452 1351422.21439275 1588350.43720119 1575522.65669578\n",
      " 1454200.72004432 1387887.59530831 1463873.32597977 1037813.76389634\n",
      " 1582409.24058838 1097822.35453873 1196264.64265796 1872937.04833347\n",
      "  647375.79969729 1224354.04836832 1295292.42603456 1234085.61887631\n",
      " 1046425.13112697  970928.25917319 1003385.66809732 1405480.98550482\n",
      "  401616.03047153 1122777.31931556 1398299.68513359 1171443.65963817\n",
      "  987666.6160517  1351581.23322852 1016994.69691976  836238.03026933\n",
      " 1009338.42813788 1670208.53304594 1519122.33899565 1746630.00012964\n",
      "  551455.49778861 1306072.16778116 1387213.72349898 1665221.19242062\n",
      " 1225609.76746002 1180732.51860578  739520.25822741 1102437.05492134\n",
      "  857270.27911179 1547328.11708601  640987.89757633 1032749.33959148\n",
      " 1144790.24786493 1534897.01057105 1138234.93236243 1811384.10710271\n",
      "  922526.3067971  1588573.59357781 1095977.78142393 1352263.95174142\n",
      "  855229.83225041 1321021.58952726 1329366.51254173 1256785.69987316\n",
      "  960037.32264917 1874389.2755079  1823422.94437553 1555547.18224453\n",
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      " 1523506.33964849 1410825.1613594  1038248.98775188  943095.15683513\n",
      " 1120445.45277418  845826.08710982 1125058.37382544  667585.83793567\n",
      " 1047122.35204459 1094240.22090633 1513682.07948245 1795694.71981195\n",
      " 1351725.04146507 1764311.56283155  426235.29009328 1600818.10529612\n",
      " 1494179.39065433  898386.99945209 1196136.60883942 1354435.43376154\n",
      " 1613800.66037941 1182535.15781556 1486487.14513521 1358996.20326848\n",
      " 1295998.52792817 1453608.34151319  443814.17289213 1081210.64313816\n",
      "  779030.25598778  814276.72814307  599079.16658795 1076949.95543929\n",
      " 1234736.26283904 1338456.80301117 1258327.24927103 1120079.1565991\n",
      " 1499879.7168487  1290704.40552833 1426626.61627864 1843754.01172954\n",
      " 1214012.64164272 1375068.99099792 1096904.25711238 1107862.99993889\n",
      "  772014.44090137 1104733.34399929 1089893.30187319  834565.49861415\n",
      " 1234385.54540177 1285776.2181272  1535198.64619251 1042577.82585166\n",
      " 1473296.01240644 1322203.59051969  935205.83538563 1511772.31482854\n",
      " 1005049.21650274 1594437.13066753 1433357.33795983 1330163.56187087\n",
      " 1292495.06583519 1102915.52981631  868963.25573898 1240223.91402268\n",
      "  913046.59376763  561331.34482757  968666.72155669 1231509.58464603\n",
      "  783525.51953925 1275447.70934418  632437.12888433 1536918.90290751\n",
      " 1309224.56711034 1281655.72093758 1352544.15897971 1425217.74585703\n",
      " 1382826.44688517 1729046.72470996 1552706.47449116  934135.42786068\n",
      " 1462629.63632893  897123.66962122 1688589.06141634 1309995.99320764\n",
      "  956387.00285118 1697237.28967003 1060720.20110856 1312876.23140968\n",
      " 1206160.20520808  433464.96778435 1647504.39875768 1295724.79305724\n",
      " 1754992.32086098 1469573.76698741 1265223.72106532 2240878.8297808\n",
      " 1308699.43876869 1614885.54732105 1020942.8209579  1865007.04255373\n",
      " 1440811.38043025 1194734.18928802 1487763.04039896 1601419.05556008\n",
      " 1985875.40492936  652029.69681704 1148757.23150024 1171553.8547024\n",
      " 1048695.94521262 1553716.56259224 1643657.97531338 1074330.64037523\n",
      " 1705764.64627321  793478.56495444 1199761.16006021  948576.34913515\n",
      " 1087613.25671736 1321632.97866599 1415334.88450649 1365902.5220095\n",
      " 1323533.30492035 1658115.8300718   939437.97381401 1323047.83197672\n",
      "  947283.56019093  858215.49595531 1067106.37906692  321618.94552973\n",
      " 1542142.99466412 1176669.05480419 1474285.27438033 1631597.84658755\n",
      " 1189764.60107247 1218867.71722548 1254643.60002393 1133966.12239794\n",
      " 1542662.12853776 1249874.8426236   512587.49510347 1260487.12399033\n",
      " 1237095.27439353 1188726.30040377 1818204.69445657  932844.45716274\n",
      " 1183504.9094332  1130987.11116025 1155228.67766157 1130242.56295993\n",
      " 1352328.40112325  780084.7993499  1388286.45031185 1108727.95708537\n",
      " 1215121.9664109  1465233.21797299 1247403.46009959 1355463.16003154\n",
      "  873323.33918075 1652563.55202158 1876465.42964879 1553350.10538147\n",
      "  946058.33856695 1250913.02320077 1269075.08138462  822979.67924419\n",
      " 1306121.93015529 1302124.15839168  979250.45614108  575893.92876236\n",
      " 1109769.74784188 1249245.21064623 1991870.66455375 1348402.0082246\n",
      " 1404456.33217934 1342287.07137709 1727387.37990905 1417632.50159095\n",
      " 1125698.34961327 1131379.85818363 1561106.13632767  980971.6363252\n",
      " 1068512.41360443  895256.40189971  485617.10530778 1319483.7387972\n",
      " 1035254.92004858 1168969.77275694 1278766.28039451 1567666.86909144\n",
      " 1052313.72246246 1479321.75418225 1576316.48992376 1327837.60953939\n",
      " 1836907.82371202  727695.77682463 1568575.2587671  1054319.11069803\n",
      "  875039.25837757  846595.4468698   860553.99632537 1079433.92878688\n",
      " 1739053.0032921  1178431.38736504 1178065.10023235 1344777.93229462\n",
      "  841588.31866877 1862078.7663361  1051929.03377105 1247138.06557954\n",
      " 1237732.54959822 1227803.91674108 1098438.09933194 1160401.33013127\n",
      " 1099710.90556335 1421597.08438094  931655.80788018 1145111.63886636\n",
      " 1230253.30905772 1252818.05321307 1093998.59083003 1354742.88798043\n",
      "  886007.85305845 1083014.18341408  861099.71156795  790615.2705201\n",
      " 1790214.97559997 1201063.98692626 1467935.84949839  777186.55822352\n",
      "  493046.13005787 1013541.49820309  951860.82698592  852932.53644316\n",
      " 1116795.80005377 1079859.38757877 1254213.7409769   869713.90382456\n",
      "  663464.88340481 1628546.47757887  997723.14485646 1476515.95949045\n",
      "  682356.5995109  1169280.45464613 1298322.41559407  728602.12069795\n",
      " 1643303.98252779 1059100.33652097 1128365.7747789  1535211.25265215\n",
      " 1235098.94288216 1355113.012617    939389.45639878  634342.87672271\n",
      " 1575197.82370387  925532.13877866 1334473.16334191  740579.28241905\n",
      "  853247.94939844 1044678.98854844 1549746.03836118  940930.21247672\n",
      " 1215327.59738433 1490199.7011461  1166308.5221569   847697.14831053\n",
      "  870493.77360818  647019.01954367 1156935.47003867 1572297.96192178\n",
      "  694516.11850108  829088.02993209  790335.52489103 1774419.13640579\n",
      " 1432461.81176284 1197954.69470097 1858272.1504985  1314707.57482256\n",
      " 1212800.80057172 1538862.45864732 1193886.26466922 1174883.77147176\n",
      " 1157509.05261446 1308500.28476256  915811.09253754 1032131.18351426\n",
      " 1671147.44548357  539172.4890111  1074078.51262118  703675.33901497\n",
      " 1410222.62802961 1658268.50458043 1125455.65399834  959791.98674344\n",
      "  697947.58483169  973240.8614885   854130.43060725 1637252.96120199\n",
      " 1268534.07133673 1232954.1501457  1075200.38321447 1239050.18847024\n",
      " 1464152.405741   1224805.02404422 1498287.52314997 1087620.61238494\n",
      "  978483.04036116 1091589.10208493 1359695.28379755  701011.83583052\n",
      " 1184804.49216406 1104206.15199608 1061964.37618722 1445207.9823986\n",
      "  628154.61202823 1413818.74466797 1309292.64567355 1624009.3870248\n",
      "  884281.97875994 1315671.57422688  882037.3452876  1213880.75473841\n",
      "  764700.70184064  970392.23576781 1096067.0952      554092.83828487\n",
      " 1750015.0458053   900575.13089245 1782864.26340864 1310104.19860118\n",
      " 1569232.96281712  927770.47452594 1437479.42675774  857093.75895878\n",
      " 1309781.71039982 1408227.94772542 1266728.71743788  656510.60944076\n",
      " 1321564.74449274  748391.4437759   531190.83560924 1227195.08166416\n",
      " 1906255.20183709 1408784.72402138  944121.70324776  778922.28245818\n",
      " 1842863.53510619  852551.86622174 1998984.04249329 1203914.68836772\n",
      " 1453661.38105508  697795.4558372   840083.78328964  806282.0253637\n",
      " 1229127.50125466 1564735.15574853 1609435.91532132  429826.61605239\n",
      " 1279423.85633239 1324459.93495184 1410928.89114077 1173277.1200435\n",
      "  888168.06603096 1120004.12785607 1146050.563426   1393133.54582383\n",
      " 1974424.68969954  844417.85988046 1269742.70473636 1458616.97126602\n",
      " 1169961.81838845 1203353.48481332  947245.3883309  1483800.16637278\n",
      " 1512918.44975118 1339678.06396422 1782788.25126482  848531.48586177\n",
      " 1106203.20006753 1135884.08404526 1324780.25779439  974800.14084121\n",
      " 1090089.65794628 1666290.18882428 1167420.22625244  858035.65163387\n",
      " 2103303.23882207 1308787.31364279 1032954.09639879  729845.25160141\n",
      "  952692.95067802  948114.80109725 1564154.15127811 1039845.29962552\n",
      " 1541852.88128162 1316877.99861091 1822478.18631297 1332186.72895592\n",
      " 1103327.71456307 1348879.02481573 1421197.54170374 1561998.96609203\n",
      "  965481.74122609 1749958.15229478 1186618.05313772 1233901.36013476\n",
      " 1103645.80253943  768916.26721135 1482560.5592684  1371046.00501875\n",
      " 1285585.04223686 1412413.14255772 1340989.46360909 1951994.60308128\n",
      " 1256545.72547916 1322748.32866065 1032962.44391574 1555401.05900394\n",
      " 1075144.77430577 1055471.31165001  668635.59018324 1526509.00875507\n",
      " 1087767.35288434 1438141.57837175 1135741.74145579 1295667.62917263\n",
      " 1050225.18109784 1661964.82035524  535954.87586374  881574.28560093\n",
      "  769982.31295609 1293232.38705973  951056.03059932 1738221.27635634\n",
      " 1376027.17247242  840390.52162004 1312685.17863874 1250955.09637823\n",
      " 1487812.56440943 1222715.87554168  637999.8016535  1934068.83366389\n",
      "  904145.75012112 1152699.3172047  2122201.53477232 1155721.34818917\n",
      " 1074703.87711527 1357944.33818852 1363085.79762958 1134682.25952267\n",
      "  444476.39918951 1194915.14709805  987915.14583509  512710.92619778\n",
      "  751837.59021993  757592.44013075  495199.26121621 1723882.24136173\n",
      " 1310687.91817889 1278968.61220396 1113612.87598267 1060862.43547224\n",
      "  952107.21956595   88843.28604113 1681361.44829249 1048784.34174574\n",
      " 1642675.63756173 1554719.82179989 1233920.39504046 1464226.31653441\n",
      " 1351041.18670605 1263347.91937607  920252.91672429  508298.66245502\n",
      " 1172235.74174838 1525223.98989107 1502304.30700022  977908.76500548\n",
      " 1027627.69640342 1852471.95474942 1185521.00741421 1148741.53846309\n",
      " 1124728.18597257 1116802.62185192 1300478.47250597 1340816.1458969\n",
      " 1754943.53480018 1898397.31901765 1346257.79531558 1232661.23464756\n",
      " 1501493.41848583 1815410.30683335  746378.94194271 1371001.79029261\n",
      "  888134.05854698 1025001.68301904 1380086.84241566 1187622.96673095\n",
      " 1311767.02703406 2179384.35086316 1071598.83864376 1444569.81886831\n",
      "  875986.88247833 1470999.11499093 1030038.49683483 1040834.88398587\n",
      " 1249449.21817851 1146794.28479763  653965.71808769 1662791.73753651\n",
      " 1114588.85260538 1090918.47573201 1094662.77285715 1335368.84966113\n",
      " 1167355.49158803 1512487.64757146 1567726.89681955  854493.64192755\n",
      " 1404562.70676956 1716794.25165157 1242624.84949534 1867271.34912179\n",
      " 1003753.56273656 1112641.21973282 1152824.26508601 1066430.71176839\n",
      " 1286312.49854127 1826310.56972385 1728027.75360327  834423.61114322\n",
      "  959879.92304315 1424392.27394186 1392689.45584084 1391515.19636056\n",
      "  959184.15853137   69997.91704466 1212770.28720426 1120915.33493395\n",
      " 1447344.86973877 1224813.61419255  965981.74068973  924953.00657535\n",
      " 1062473.87199954 1350495.12083731 1277201.86851426 1046254.4236585\n",
      "  867914.66199135  734413.00894406 1381324.71323601 1117744.91286519\n",
      " 2003236.62309106  943262.43227807 1353072.45516217 1561552.64363492\n",
      " 1560882.62450411  879515.95314118 1126469.35795986  834003.0303598\n",
      " 1590686.09783476  918567.11777883 1159060.84764842 1017309.63822469\n",
      " 1800012.80366664 1386188.5425781   463967.62828745  950493.29566874\n",
      " 1759742.65465661  825152.88478396 1678258.76114196 1061704.81692301\n",
      "  573366.20923435 1346403.29612037 1319586.98229917 1245242.44451345\n",
      "  994916.15856854 1174824.23153441 1430236.65493593 1118565.03562639]\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Accuracy score is:\",reg.score(x_train,y_train)*100)\n",
    "# Checking predicted values\n",
    "predict=reg.predict(x_test)\n",
    "print(\"Predicted values are:\\n\")\n",
    "print(predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for Error<br>\n",
    "Error is less as accuracy is more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared error is: 10516537448.56889\n",
      "Mean Absolute error is: 82805.16769014584\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Squared error is:\",mean_squared_error(y_test,predict))\n",
    "print(\"Mean Absolute error is:\",mean_absolute_error(y_test,predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting graph for complete dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PCA (Principle component analysis)- For Dimension reduction of data<br>\n",
    "As we cannot plot with multiple dimensions thus dimensions will be reduced to 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transforming data\n",
      " [[-12355.34231389  11769.41434607]\n",
      " [-10150.51349937  -5176.60316217]\n",
      " [  7330.64748761    101.39871032]\n",
      " ...\n",
      " [  4836.0192449    3459.61287124]\n",
      " [  1300.45425077  -6356.57719758]\n",
      " [  4208.80933424  -9929.54125856]]\n",
      "\n",
      "Shape of Transformed data is:\n",
      " (5000, 2)\n",
      "\n",
      "Transformed Data is:\n",
      " [[-12355.34231389  11769.41434607]\n",
      " [-10150.51349937  -5176.60316217]\n",
      " [  7330.64748761    101.39871032]\n",
      " ...\n",
      " [  4836.0192449    3459.61287124]\n",
      " [  1300.45425077  -6356.57719758]\n",
      " [  4208.80933424  -9929.54125856]]\n"
     ]
    }
   ],
   "source": [
    "x=PCA(n_components=2)\n",
    "# Transforming data\n",
    "pca_data=x.fit_transform(X)\n",
    "print(\"Transforming data\\n\",x.fit_transform(X))\n",
    "print(\"\\nShape of Transformed data is:\\n\",pca_data.shape)\n",
    "print(\"\\nTransformed Data is:\\n\",pca_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transforming testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transforming data\n",
      " [[ 1771.81565453  5495.59774127]\n",
      " [-9492.20122142 -6862.58130411]\n",
      " [ 9055.82212995  2662.43902172]\n",
      " ...\n",
      " [ 9012.83961128  8681.6642555 ]\n",
      " [-4893.35500957 -1934.49225898]\n",
      " [-1427.45082022  2638.9649737 ]]\n",
      "\n",
      "Shape of Transformed data is:\n",
      " (1000, 2)\n",
      "\n",
      "Transformed Data is:\n",
      " [[ 1771.81565453  5495.59774127]\n",
      " [-9492.20122142 -6862.58130411]\n",
      " [ 9055.82212995  2662.43902172]\n",
      " ...\n",
      " [ 9012.83961128  8681.6642555 ]\n",
      " [-4893.35500957 -1934.49225898]\n",
      " [-1427.45082022  2638.9649737 ]]\n"
     ]
    }
   ],
   "source": [
    "x=PCA(n_components=2)\n",
    "pca_data_2 = x.fit_transform(x_test)\n",
    "print(\"Transforming data\\n\",x.fit_transform(x_test))\n",
    "print(\"\\nShape of Transformed data is:\\n\",pca_data_2.shape)\n",
    "print(\"\\nTransformed Data is:\\n\",pca_data_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting<br>\n",
    "We can see the points are closer to line<br>\n",
    "Cost function is very low<br>\n",
    "This is why absolute and mean squared error is low<br>\n",
    "Since we have multiple Independent Variable, the graph contains multiple regression lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": [
    "plt.scatter(pca_data[:,0],pca_data[:,1],color=\"blue\")\n",
    "plt.plot(pca_data_2[:,0],pca_data_2[:,1],color=\"red\")\n",
    "plt.legend(['Actual Values','Best Line or General formula'])\n",
    "plt.title(\"Regression Plot of Input Parameters VS Price\")\n",
    "plt.xlabel(\"Input Parameters\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}