{ "cells": [ { "cell_type": "code", "execution_count": 12, "id": "b040cb60", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 5, "id": "468e8279", "metadata": {}, "outputs": [], "source": [ "df=pd.read_csv('HousingData.csv')" ] }, { "cell_type": "code", "execution_count": 6, "id": "162fe503", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
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" ], "text/plain": [ " CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO \\\n", "501 0.06263 0.0 11.93 0.0 0.573 6.593 69.1 2.4786 1 273 21.0 \n", "502 0.04527 0.0 11.93 0.0 0.573 6.120 76.7 2.2875 1 273 21.0 \n", "503 0.06076 0.0 11.93 0.0 0.573 6.976 91.0 2.1675 1 273 21.0 \n", "504 0.10959 0.0 11.93 0.0 0.573 6.794 89.3 2.3889 1 273 21.0 \n", "505 0.04741 0.0 11.93 0.0 0.573 6.030 NaN 2.5050 1 273 21.0 \n", "\n", " B LSTAT MEDV \n", "501 391.99 NaN 22.4 \n", "502 396.90 9.08 20.6 \n", "503 396.90 5.64 23.9 \n", "504 393.45 6.48 22.0 \n", "505 396.90 7.88 11.9 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail()" ] }, { "cell_type": "code", "execution_count": 9, "id": "9e4fd7cb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 506 entries, 0 to 505\n", "Data columns (total 14 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 CRIM 486 non-null float64\n", " 1 ZN 486 non-null float64\n", " 2 INDUS 486 non-null float64\n", " 3 CHAS 486 non-null float64\n", " 4 NOX 506 non-null float64\n", " 5 RM 506 non-null float64\n", " 6 AGE 486 non-null float64\n", " 7 DIS 506 non-null float64\n", " 8 RAD 506 non-null int64 \n", " 9 TAX 506 non-null int64 \n", " 10 PTRATIO 506 non-null float64\n", " 11 B 506 non-null float64\n", " 12 LSTAT 486 non-null float64\n", " 13 MEDV 506 non-null float64\n", "dtypes: float64(12), int64(2)\n", "memory usage: 55.5 KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 10, "id": "49ac679d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
count486.000000486.000000486.000000486.000000506.000000506.000000486.000000506.000000506.000000506.000000506.000000506.000000486.000000506.000000
mean3.61187411.21193411.0839920.0699590.5546956.28463468.5185193.7950439.549407408.23715418.455534356.67403212.71543222.532806
std8.72019223.3888766.8358960.2553400.1158780.70261727.9995132.1057108.707259168.5371162.16494691.2948647.1558719.197104
min0.0063200.0000000.4600000.0000000.3850003.5610002.9000001.1296001.000000187.00000012.6000000.3200001.7300005.000000
25%0.0819000.0000005.1900000.0000000.4490005.88550045.1750002.1001754.000000279.00000017.400000375.3775007.12500017.025000
50%0.2537150.0000009.6900000.0000000.5380006.20850076.8000003.2074505.000000330.00000019.050000391.44000011.43000021.200000
75%3.56026312.50000018.1000000.0000000.6240006.62350093.9750005.18842524.000000666.00000020.200000396.22500016.95500025.000000
max88.976200100.00000027.7400001.0000000.8710008.780000100.00000012.12650024.000000711.00000022.000000396.90000037.97000050.000000
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" ], "text/plain": [ " CRIM ZN INDUS CHAS NOX RM \\\n", "count 486.000000 486.000000 486.000000 486.000000 506.000000 506.000000 \n", "mean 3.611874 11.211934 11.083992 0.069959 0.554695 6.284634 \n", "std 8.720192 23.388876 6.835896 0.255340 0.115878 0.702617 \n", "min 0.006320 0.000000 0.460000 0.000000 0.385000 3.561000 \n", "25% 0.081900 0.000000 5.190000 0.000000 0.449000 5.885500 \n", "50% 0.253715 0.000000 9.690000 0.000000 0.538000 6.208500 \n", "75% 3.560263 12.500000 18.100000 0.000000 0.624000 6.623500 \n", "max 88.976200 100.000000 27.740000 1.000000 0.871000 8.780000 \n", "\n", " AGE DIS RAD TAX PTRATIO B \\\n", "count 486.000000 506.000000 506.000000 506.000000 506.000000 506.000000 \n", "mean 68.518519 3.795043 9.549407 408.237154 18.455534 356.674032 \n", "std 27.999513 2.105710 8.707259 168.537116 2.164946 91.294864 \n", "min 2.900000 1.129600 1.000000 187.000000 12.600000 0.320000 \n", "25% 45.175000 2.100175 4.000000 279.000000 17.400000 375.377500 \n", "50% 76.800000 3.207450 5.000000 330.000000 19.050000 391.440000 \n", "75% 93.975000 5.188425 24.000000 666.000000 20.200000 396.225000 \n", "max 100.000000 12.126500 24.000000 711.000000 22.000000 396.900000 \n", "\n", " LSTAT MEDV \n", "count 486.000000 506.000000 \n", "mean 12.715432 22.532806 \n", "std 7.155871 9.197104 \n", "min 1.730000 5.000000 \n", "25% 7.125000 17.025000 \n", "50% 11.430000 21.200000 \n", "75% 16.955000 25.000000 \n", "max 37.970000 50.000000 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 16, "id": "d1103da5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CRIM 20\n", "ZN 20\n", "INDUS 20\n", "CHAS 20\n", "NOX 0\n", "RM 0\n", "AGE 20\n", "DIS 0\n", "RAD 0\n", "TAX 0\n", "PTRATIO 0\n", "B 0\n", "LSTAT 20\n", "MEDV 0\n", "dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 17, "id": "82b4bf57", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(506, 14)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 19, "id": "b0720e0f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average is: 3.6118739711934156\n" ] } ], "source": [ "Avg_CRIM = df[\"CRIM\"].astype(\"float\").mean(axis=0)\n", "print(\"Average is:\",Avg_CRIM)" ] }, { "cell_type": "code", "execution_count": 21, "id": "da13bf95", "metadata": {}, "outputs": [], "source": [ "df[\"CRIM\"].replace(np.nan,Avg_CRIM,inplace = True)" ] }, { "cell_type": "code", "execution_count": 24, "id": "5e099f1a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average is: 68.51851851851852\n" ] } ], "source": [ "Avg_AGE = df[\"AGE\"].astype(\"float\").mean(axis=0)\n", "print(\"Average is:\",Avg_AGE)" ] }, { "cell_type": "code", "execution_count": 25, "id": "580b98aa", "metadata": {}, "outputs": [], "source": [ "df[\"AGE\"].replace(np.nan,Avg_AGE,inplace = True)" ] }, { "cell_type": "code", "execution_count": 26, "id": "953b4e32", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average is: 11.083991769547325\n" ] } ], "source": [ "Avg_INDUS = df[\"INDUS\"].astype(\"float\").mean(axis=0)\n", "print(\"Average is:\",Avg_INDUS)\n", "df[\"INDUS\"].replace(np.nan,Avg_INDUS,inplace = True)" ] }, { "cell_type": "code", "execution_count": 27, "id": "f42c6add", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average is: 0.06995884773662552\n" ] } ], "source": [ "Avg_CHAS = df[\"CHAS\"].astype(\"float\").mean(axis=0)\n", "print(\"Average is:\",Avg_CHAS)\n", "df[\"CHAS\"].replace(np.nan,Avg_CHAS,inplace = True)" ] }, { "cell_type": "code", "execution_count": 28, "id": "bc392a07", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average is: 11.2119341563786\n" ] } ], "source": [ "Avg_ZN = df[\"ZN\"].astype(\"float\").mean(axis=0)\n", "print(\"Average is:\",Avg_ZN)\n", "df[\"ZN\"].replace(np.nan,Avg_ZN,inplace = True)" ] }, { "cell_type": "code", "execution_count": 30, "id": "b7daf506", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average is: 11.211934156378602\n" ] } ], "source": [ "Avg_ZN = df[\"ZN\"].astype(\"float\").mean(axis=0)\n", "print(\"Average is:\",Avg_ZN)\n", "df[\"ZN\"].replace(np.nan,Avg_ZN,inplace = True)" ] }, { "cell_type": "code", "execution_count": 31, "id": "cb3c7e80", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average is: 12.715432098765433\n" ] } ], "source": [ "Avg_LSTAT = df[\"LSTAT\"].astype(\"float\").mean(axis=0)\n", "print(\"Average is:\",Avg_LSTAT)\n", "df[\"LSTAT\"].replace(np.nan,Avg_LSTAT,inplace = True)" ] }, { "cell_type": "code", "execution_count": 32, "id": "3e188bb1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CRIM 0\n", "ZN 0\n", "INDUS 0\n", "CHAS 0\n", "NOX 0\n", "RM 0\n", "AGE 0\n", "DIS 0\n", "RAD 0\n", "TAX 0\n", "PTRATIO 0\n", "B 0\n", "LSTAT 0\n", "MEDV 0\n", "dtype: int64" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 34, "id": "3d8e514d", "metadata": {}, "outputs": [], "source": [ "X = df.iloc[:,0:13]\n", "y = df.iloc[:,-1]" ] }, { "cell_type": "code", "execution_count": 35, "id": "45e722c1", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20,random_state=42)" ] }, { "cell_type": "code", "execution_count": 36, "id": "40581f6f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(404, 13)\n", "(102, 13)\n", "(404,)\n", "(102,)\n" ] } ], "source": [ "print(X_train.shape)\n", "print(X_test.shape)\n", "print(y_train.shape)\n", "print(y_test.shape)" ] }, { "cell_type": "code", "execution_count": 37, "id": "1aaae298", "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression" ] }, { "cell_type": "code", "execution_count": 38, "id": "a3d7dd25", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('standardscaler', StandardScaler(with_mean=False)),\n",
       "                ('linearregression', LinearRegression())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" ], "text/plain": [ "Pipeline(steps=[('standardscaler', StandardScaler(with_mean=False)),\n", " ('linearregression', LinearRegression())])" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import StandardScaler\n", "from sklearn.pipeline import make_pipeline\n", "model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())\n", "model.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 39, "id": "6b7da0bb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6588520195508119" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.score(X_test,y_test)" ] }, { "cell_type": "code", "execution_count": null, "id": "e7033244", "metadata": {}, "outputs": [], "source": [] } ], "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }