From 188e23349bb1a242afdb575ee23d91a21c6ab685 Mon Sep 17 00:00:00 2001
From: Shubhi Srivastava <72998724+shubhi13@users.noreply.github.com>
Date: Sat, 30 Oct 2021 02:09:00 +0530
Subject: [PATCH] Polynomial Regression using Python
---
.../Polynomial Regression/Boston.ns.csv | 507 ++++++++++
.../Polynomial Regression.ipynb | 877 ++++++++++++++++++
2 files changed, 1384 insertions(+)
create mode 100644 Machine Learning/Polynomial Regression/Boston.ns.csv
create mode 100644 Machine Learning/Polynomial Regression/Polynomial Regression.ipynb
diff --git a/Machine Learning/Polynomial Regression/Boston.ns.csv b/Machine Learning/Polynomial Regression/Boston.ns.csv
new file mode 100644
index 0000000..8c2d22a
--- /dev/null
+++ b/Machine Learning/Polynomial Regression/Boston.ns.csv
@@ -0,0 +1,507 @@
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diff --git a/Machine Learning/Polynomial Regression/Polynomial Regression.ipynb b/Machine Learning/Polynomial Regression/Polynomial Regression.ipynb
new file mode 100644
index 0000000..fcaab1a
--- /dev/null
+++ b/Machine Learning/Polynomial Regression/Polynomial Regression.ipynb
@@ -0,0 +1,877 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Shubhi Srivastava\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.linear_model import LinearRegression\n",
+ "from sklearn.preprocessing import PolynomialFeatures\n",
+ "from sklearn.metrics import mean_squared_error, r2_score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bn=pd.read_csv(\"Boston.ns.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
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+ "
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+ " zn | \n",
+ " indus | \n",
+ " chas | \n",
+ " nox | \n",
+ " rm | \n",
+ " age | \n",
+ " dis | \n",
+ " rad | \n",
+ " tax | \n",
+ " ptratio | \n",
+ " black | \n",
+ " lstat | \n",
+ " medv | \n",
+ "
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+ " 1 | \n",
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+ " | 502 | \n",
+ " 503 | \n",
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+ " 273 | \n",
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+ " | 503 | \n",
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+ " | 504 | \n",
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+ " | 505 | \n",
+ " 506 | \n",
+ " 0.04741 | \n",
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+ " 0.573 | \n",
+ " 6.030 | \n",
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+ "
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+ " \n",
+ "
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+ "
506 rows × 15 columns
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+ "
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+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10,10))\n",
+ "correlation_matrix=bn.corr().round(2)\n",
+ "sns.heatmap(data=correlation_matrix,annot=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(506, 4)\n",
+ "(506,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "X = pd.DataFrame(np.c_[bn['lstat'], bn['rm'], bn['crim'], bn['nox']])\n",
+ "Y = bn['medv']\n",
+ "\n",
+ "print(X.shape)\n",
+ "print(Y.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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