From aed3924249e3c7a9fee4fe3b3ba97ec1ff84f238 Mon Sep 17 00:00:00 2001
From: SUBZZGITHUB <58504552+SUBZZGITHUB@users.noreply.github.com>
Date: Sat, 30 Jan 2021 02:34:04 +0530
Subject: [PATCH 1/3] Add files via upload

---
 .../Code/salaries(SN).ipynb                   | 301 ++++++++++++++++++
 1 file changed, 301 insertions(+)
 create mode 100644 Linear Regression Task/Code/salaries(SN).ipynb

diff --git a/Linear Regression Task/Code/salaries(SN).ipynb b/Linear Regression Task/Code/salaries(SN).ipynb
new file mode 100644
index 0000000..00f2ec6
--- /dev/null
+++ b/Linear Regression Task/Code/salaries(SN).ipynb	
@@ -0,0 +1,301 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "data = pd.read_csv('Salary_Data.csv')\n",
+    "x = data.iloc[:, :-1].values\n",
+    "y = data.iloc[:,1].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>YearsExperience</th>\n",
+       "      <th>Salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.1</td>\n",
+       "      <td>39343.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.3</td>\n",
+       "      <td>46205.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>37731.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>43525.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.2</td>\n",
+       "      <td>39891.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   YearsExperience   Salary\n",
+       "0              1.1  39343.0\n",
+       "1              1.3  46205.0\n",
+       "2              1.5  37731.0\n",
+       "3              2.0  43525.0\n",
+       "4              2.2  39891.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(30, 2)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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=1/3,random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "rgr=LinearRegression()\n",
+    "rgr.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = rgr.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x2809da3f910>"
+      ]
+     },
+     "execution_count": 19,
+     "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_train,y_train,color='red')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x2809da97790>"
+      ]
+     },
+     "execution_count": 20,
+     "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='red')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "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)\n",
+    "plt.plot(x_test,y_pred,color='blue')\n",
+    "plt.title(\"Linear Regression\")\n",
+    "plt.xlabel('Years of Experience')\n",
+    "plt.ylabel('Salary')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 sq. value: 0.9749154407708353\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "print('R2 sq. value:',metrics.r2_score(y_test,y_pred))"
+   ]
+  },
+  {
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 47b08d30375625ac259ba445c47a9c7a4c62baa4 Mon Sep 17 00:00:00 2001
From: SUBZZGITHUB <58504552+SUBZZGITHUB@users.noreply.github.com>
Date: Sat, 30 Jan 2021 02:34:47 +0530
Subject: [PATCH 2/3] Add files via upload

---
 .../Dataset/Salary_Data.csv                   | 31 +++++++++++++++++++
 1 file changed, 31 insertions(+)
 create mode 100644 Linear Regression Task/Dataset/Salary_Data.csv

diff --git a/Linear Regression Task/Dataset/Salary_Data.csv b/Linear Regression Task/Dataset/Salary_Data.csv
new file mode 100644
index 0000000..a6863aa
--- /dev/null
+++ b/Linear Regression Task/Dataset/Salary_Data.csv	
@@ -0,0 +1,31 @@
+YearsExperience,Salary
+1.1,39343.00
+1.3,46205.00
+1.5,37731.00
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From 33a965dcb91ed5a65fefde274fa47fbd252b89ad Mon Sep 17 00:00:00 2001
From: SUBZZGITHUB <58504552+SUBZZGITHUB@users.noreply.github.com>
Date: Sat, 30 Jan 2021 02:35:15 +0530
Subject: [PATCH 3/3] Add files via upload

---
 .../Project Report(SN)(Salaries).ipynb        | 319 ++++++++++++++++++
 1 file changed, 319 insertions(+)
 create mode 100644 Linear Regression Task/Project Report/Project Report(SN)(Salaries).ipynb

diff --git a/Linear Regression Task/Project Report/Project Report(SN)(Salaries).ipynb b/Linear Regression Task/Project Report/Project Report(SN)(Salaries).ipynb
new file mode 100644
index 0000000..881f867
--- /dev/null
+++ b/Linear Regression Task/Project Report/Project Report(SN)(Salaries).ipynb	
@@ -0,0 +1,319 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Project Report\n",
+    " \n",
+    " This is a project report for Simple Linear Regression.\n",
+    " I have used the Salaries dataset available in Kaggle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The dataset is loaded\n",
+    "import pandas as pd\n",
+    "data = pd.read_csv('Salary_Data.csv')\n",
+    "x = data.iloc[:, :-1].values  # x variable is YearsExperience\n",
+    "y = data.iloc[:,1].values     #y variable is Salary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>YearsExperience</th>\n",
+       "      <th>Salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.1</td>\n",
+       "      <td>39343.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.3</td>\n",
+       "      <td>46205.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>37731.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>43525.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.2</td>\n",
+       "      <td>39891.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   YearsExperience   Salary\n",
+       "0              1.1  39343.0\n",
+       "1              1.3  46205.0\n",
+       "2              1.5  37731.0\n",
+       "3              2.0  43525.0\n",
+       "4              2.2  39891.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# data visualisation\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(30, 2)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Spliting data into training and testing\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=1/3,random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fitting the Simple Linear Regression model to training set\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "rgr=LinearRegression()\n",
+    "rgr.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make Prediction on the testing set\n",
+    "y_pred = rgr.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For visualizing the results \n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x2809da3f910>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAD4CAYAAADy46FuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUjklEQVR4nO3db4xddX7f8ffHuCXrjWD5YxC1wUOEtSmsmm4YEZKVoiiExVJWCw+IZGm2WCmSVYSSbbZSCuUBUiKqRY1KwgOQLNisCSNY6m4FigpZy1TaJwQyLFH4txRvwcbBwY7MUhRLJOBvH5wz9fV4fDyeGd9zZ+77JV2de7/3nuPfHcH93HN+v9/9paqQJOlU1vTdAEnSaDMoJEmdDApJUieDQpLUyaCQJHVa23cDltvFF19cExMTfTdDklaUl19++e+rav18z626oJiYmGBmZqbvZkjSipJk36me89KTJKmTQSFJ6mRQSJI6GRSSpE4GhSSpk0EhSSvd9DRMTMCaNc12enpZD7/qhsdK0liZnobt2+Ho0ebxvn3NY4CpqWX5JzyjkKSV7J57jofErKNHm/oyMSgkaSXbv//M6otgUEjSSnbFFWdWX4TTBkWS7yQ5lOS1gdp/SfLjJH+T5H8k+cLAc3cn2ZvkrSQ3DdSvTfJq+9yDSdLWz03yvbb+YpKJgX22JXm7vW1brjctSavGfffBunUn1tata+rLZCFnFN8Ftsyp7Qa+VFX/CvjfwN0ASa4GtgLXtPs8lOScdp+Hge3A5vY2e8zbgQ+r6irgAeD+9lgXAvcCvwRcB9yb5IIzf4uStIpNTcGOHbBpEyTNdseOZevIhgUERVX9EDgyp/aDqvq0ffiXwMb2/s3Ak1X1SVW9A+wFrktyGXBeVb1QzSLdjwG3DOyzs72/C7ihPdu4CdhdVUeq6kOacJobWJKkqSl49104dqzZLmNIwPL0Ufxb4Nn2/gbgvYHnDrS1De39ufUT9mnD5yPgoo5jnSTJ9iQzSWYOHz68pDcjSTrRkoIiyT3Ap8Ds7I7M87LqqC92nxOLVTuqarKqJtevn/fn1CVJi7TooGg7l78GTLWXk6D51n/5wMs2Au+39Y3z1E/YJ8la4HyaS12nOpYkaYgWFRRJtgD/Efh6VQ3O9HgG2NqOZLqSptP6pao6CHyc5Pq2/+E24OmBfWZHNN0KPN8Gz18AX01yQduJ/dW2JkkaotP+hEeSJ4BfAy5OcoBmJNLdwLnA7naU619W1b+rqteTPAW8QXNJ6s6q+qw91B00I6g+R9OnMduv8SjwZ0n20pxJbAWoqiNJ/hD4q/Z1f1BVJ3SqS5LOvhy/arQ6TE5OlkuhStKZSfJyVU3O95wzsyVJnQwKSVIng0KS1MmgkCR1MigkSZ0MCklSJ4NC0ug6y2tBa2FcM1vSaBrCWtBaGM8oJI2mIawFrYUxKCSNpiGsBa2FMSgkjaYhrAWthTEoJI2mIawFrYUxKCSNpiGsBa2FcdSTpNE1NWUwjADPKCRJnQwKSVIng0KS1MmgkCR1MigkSZ0MCklSJ4NCktTJoJAkdTIoJEmdDApJUieDQpLUyaCQNN5cbvW0/FFASePL5VYXxDMKSePL5VYXxKCQNL5cbnVBDApJ48vlVhfEoJA0vlxudUEMCknjy+VWF8RRT5LGm8utnpZnFJKkTgaFJKmTQSFJ6mRQSJI6GRSSpE6nDYok30lyKMlrA7ULk+xO8na7vWDgubuT7E3yVpKbBurXJnm1fe7BJGnr5yb5Xlt/McnEwD7b2n/j7STblutNS5IWbiFnFN8Ftsyp3QXsqarNwJ72MUmuBrYC17T7PJTknHafh4HtwOb2NnvM24EPq+oq4AHg/vZYFwL3Ar8EXAfcOxhIkqThOG1QVNUPgSNzyjcDO9v7O4FbBupPVtUnVfUOsBe4LsllwHlV9UJVFfDYnH1mj7ULuKE927gJ2F1VR6rqQ2A3JweWJOksW2wfxaVVdRCg3V7S1jcA7w287kBb29Den1s/YZ+q+hT4CLio41iSpCFa7s7szFOrjvpi9znxH022J5lJMnP48OEFNVSStDCLDYoP2stJtNtDbf0AcPnA6zYC77f1jfPUT9gnyVrgfJpLXac61kmqakdVTVbV5Pr16xf5liS52pvms9igeAaYHYW0DXh6oL61Hcl0JU2n9Uvt5amPk1zf9j/cNmef2WPdCjzf9mP8BfDVJBe0ndhfbWuSzobZ1d727YOq46u9GRZjbyHDY58AXgC+mORAktuBbwM3JnkbuLF9TFW9DjwFvAE8B9xZVZ+1h7oDeISmg/snwLNt/VHgoiR7gW/RjqCqqiPAHwJ/1d7+oK1JOhtc7U2nkObL++oxOTlZMzMzfTdDWnnWrGnOJOZK4Nix4bdHQ5Xk5aqanO85Z2ZLarjam07BoJDUcLU3nYJBIanham86BVe4k3Scq71pHp5RSJI6GRSSpE4GhdQnZ0JrBbCPQurL7Ezo2UluszOhwX4CjRTPKKS+OBNaK4RBIfVl//4zq0s9MSikvjgTWiuEQSH1ZZgzoe001xIYFFJfhjUT2p8P1xL567HSajcx0YTDXJs2wbvvDrs1GlH+eqw0zuw01xIZFNJqZ6e5lsigkFY7fz5cS2RQSKudPx+uJfInPKRx4M+Hawk8o5AkdTIoJEmdDApJUieDQpLUyaCQJHUyKCRJnQwKSVIng0KS1MmgkCR1MigkSZ0MCklSJ4NCktTJoJAkdTIoJEmdDApJUieDQpLUyaCQJHUyKKRhm56GiQlYs6bZTk/33SKpk0uhSsM0PQ3bt8PRo83jffuax+BSpRpZnlFIw3TPPcdDYtbRo01dGlFLCookv5fk9SSvJXkiyc8kuTDJ7iRvt9sLBl5/d5K9Sd5KctNA/dokr7bPPZgkbf3cJN9r6y8mmVhKe6Xe7d9/ZnVpBCw6KJJsAH4XmKyqLwHnAFuBu4A9VbUZ2NM+JsnV7fPXAFuAh5Kc0x7uYWA7sLm9bWnrtwMfVtVVwAPA/YttrzQSrrjizOrSCFjqpae1wOeSrAXWAe8DNwM72+d3Are0928GnqyqT6rqHWAvcF2Sy4DzquqFqirgsTn7zB5rF3DD7NmGtCLddx+sW3dibd26pi6NqEUHRVX9LfBHwH7gIPBRVf0AuLSqDravOQhc0u6yAXhv4BAH2tqG9v7c+gn7VNWnwEfARYtts9S7qSnYsQM2bYKk2e7YYUe2RtqiRz21fQ83A1cCPwX+W5JvdO0yT6066l37zG3LdppLV1zhKbxG3dSUwaAVZSmXnn4DeKeqDlfVPwHfB34F+KC9nES7PdS+/gBw+cD+G2kuVR1o78+tn7BPe3nrfODI3IZU1Y6qmqyqyfXr1y/hLUmS5lpKUOwHrk+yru03uAF4E3gG2Na+ZhvwdHv/GWBrO5LpSppO65fay1MfJ7m+Pc5tc/aZPdatwPNtP4YkaUgWfempql5Msgv4EfAp8AqwA/hZ4Kkkt9OEyW+1r389yVPAG+3r76yqz9rD3QF8F/gc8Gx7A3gU+LMke2nOJLYutr2SpMXJavuCPjk5WTMzM303Q5JWlCQvV9XkfM85M1uS1MmgkCR1MigkSZ0MCklSJ4NCktTJoJAkdTIoJEmdDApJUieDQpLUyaCQJHUyKCRJnQwKSVIng0KS1MmgkCR1MigkSZ0MCklSJ4NCktTJoJAkdTIoJEmdDApJUieDQuNnehomJmDNmmY7Pd13i6SRtrbvBkhDNT0N27fD0aPN4337mscAU1P9tUsaYZ5RaLzcc8/xkJh19GhTlzQvg0LjZf/+M6tLMig0Zq644szqkgwKjZn77oN1606srVvX1O3kluZlUGi8TE3Bjh2waRMkzXbHjua57dubzu2q453choVEqqrvNiyrycnJmpmZ6bsZWmkmJppwmGvTJnj33WG3Rhq6JC9X1eR8z3lGIYGd3FIHg0LzG7fr9XZyS6dkUOhks5PSxul6fVcntzTmDAqdbBwnpZ2qk9vZ2pKd2ZrHmjXNmcRcCRw7Nvz2SDrr7MzWmfF6vaQBBoVO5vV6SQMMCp3M6/WSBvgz45rf1JTBIAnwjEIr3bjN95B64BmFVi4XIZKGYklnFEm+kGRXkh8neTPJLye5MMnuJG+32wsGXn93kr1J3kpy00D92iSvts89mCRt/dwk32vrLyaZWEp7NcdK/zY+jvM9pB4s9dLTnwDPVdXPA78AvAncBeypqs3AnvYxSa4GtgLXAFuAh5Kc0x7nYWA7sLm9bWnrtwMfVtVVwAPA/Utsr2athtnX/j6TNBSLDook5wG/CjwKUFX/WFU/BW4GdrYv2wnc0t6/GXiyqj6pqneAvcB1SS4DzquqF6qZ/ffYnH1mj7ULuGH2bENLtBq+jTvfQxqKpZxR/BxwGPjTJK8keSTJ54FLq+ogQLu9pH39BuC9gf0PtLUN7f259RP2qapPgY+Ai+Y2JMn2JDNJZg4fPryEtzRGVsO3ced7SEOxlKBYC/wi8HBVfRn4B9rLTKcw35lAddS79jmxULWjqiaranL9+vXdrVZjNXwbd76HNBRLCYoDwIGqerF9vIsmOD5oLyfRbg8NvP7ygf03Au+39Y3z1E/YJ8la4HzgyBLarFmr5dv41FSzsNCxY83WkJCW3aKDoqr+DngvyRfb0g3AG8AzwLa2tg14ur3/DLC1Hcl0JU2n9Uvt5amPk1zf9j/cNmef2WPdCjxfq+1XDPvit3FJC7TUUU+/A0wn+RvgXwP/Gfg2cGOSt4Eb28dU1evAUzRh8hxwZ1V91h7nDuARmg7unwDPtvVHgYuS7AW+RfelLZ2ps/ltfKUPvZV0XFWtqtu1115b6vD441WbNlUlzfbxx8/Ov7FuXVUz8La5rVs3/781jPZIOi1gpk7xuep6FONk7kxmaPollvuS08REMy9jrk2bmjOXYbdH0ml1rUdhUIyThX6AL9VCFz4aVnsknZYLF6kxrLkTCx16uxrmckhjwKAYJ8OaO7HQoberYS6HNAYMinEyrLkTCx16u1rmckirnEExTs5k7sRSh7cuZOitczmkFcHObJ3M0UjS2LEzW2dmNfyyrKRlY1DoZI5GkjTAoNDJHI0kaYBBoZM5GknSAINCJ3M0kqQBa/tugEbU1JTBIAnwjEKSdBoGhSSpk0EhSepkUEiSOhkUkqROBoUkqZNBIUnqZFBIkjoZFJKkTgaFJKmTQSFJ6mRQnM5SlwSVpBXOHwXsMndJ0H37msfgD+ZJGhueUXRxSVBJMig6uSSoJBkUnVwSVJIMik4uCSpJBkUnlwSVJIPitKam4N134dixZrucIeHQW0krgMNj++LQW0krhGcUfXHoraQVwqDoi0NvJa0QBkVfHHoraYUwKPri0FtJK4RB0ReH3kpaIRz11KepKYNB0shb8hlFknOSvJLkz9vHFybZneTtdnvBwGvvTrI3yVtJbhqoX5vk1fa5B5OkrZ+b5Htt/cUkE0ttryTpzCzHpadvAm8OPL4L2FNVm4E97WOSXA1sBa4BtgAPJTmn3edhYDuwub1taeu3Ax9W1VXAA8D9y9De0edEPEkjZElBkWQj8JvAIwPlm4Gd7f2dwC0D9Ser6pOqegfYC1yX5DLgvKp6oaoKeGzOPrPH2gXcMHu2sexG5cN5diLevn1QdXwinmEhqSdLPaP4Y+D3gWMDtUur6iBAu72krW8A3ht43YG2tqG9P7d+wj5V9SnwEXDR3EYk2Z5kJsnM4cOHz/xdjNKHsxPxJI2YRQdFkq8Bh6rq5YXuMk+tOupd+5xYqNpRVZNVNbl+/foFNmfAKH04OxFP0ohZyhnFV4CvJ3kXeBL49SSPAx+0l5Not4fa1x8ALh/YfyPwflvfOE/9hH2SrAXOB44soc3zG6UPZyfiSRoxiw6Kqrq7qjZW1QRNJ/XzVfUN4BlgW/uybcDT7f1ngK3tSKYraTqtX2ovT32c5Pq2/+G2OfvMHuvW9t846YxiyUbpw9mJeJJGzNmYcPdt4MYkbwM3to+pqteBp4A3gOeAO6vqs3afO2g6xPcCPwGebeuPAhcl2Qt8i3YE1bIbpQ9nJ+JJGjE5G1/Q+zQ5OVkzMzNnvuP0dNMnsX9/cyZx331+OEsaG0lerqrJ+Z5zZvYsZ0lL0rz8rSdJUieDQpLUyaCQJHUyKCRJnQwKSVKnVTc8NslhYF/f7ThDFwN/33cjejbuf4Nxf//g3wD6/Rtsqqp5fwNp1QXFSpRk5lTjl8fFuP8Nxv39g38DGN2/gZeeJEmdDApJUieDYjTs6LsBI2Dc/wbj/v7BvwGM6N/APgpJUifPKCRJnQwKSVIng6InSS5P8r+SvJnk9STf7LtNfUlyTpJXkvx5323pQ5IvJNmV5Mftfw+/3Hebhi3J77X/H7yW5IkkP9N3m862JN9JcijJawO1C5PsTvJ2u72gzzbOMij68ynwH6rqXwLXA3cmubrnNvXlm8CbfTeiR38CPFdVPw/8AmP2t0iyAfhdYLKqvgScQ7Nq5mr3XWDLnNpdwJ6q2gzs4Wwt1naGDIqeVNXBqvpRe/9jmg+HDf22aviSbAR+k2aFw7GT5DzgV2lWc6Sq/rGqftpvq3qxFvhckrXAOuD9nttz1lXVD4Ejc8o3Azvb+zuBW4baqFMwKEZAkgngy8CL/bakF38M/D5wrO+G9OTngMPAn7aX3x5J8vm+GzVMVfW3wB8B+4GDwEdV9YN+W9WbS6vqIDRfJoFLem4PYFD0LsnPAv8d+PdV9X/7bs8wJfkacKiqXu67LT1aC/wi8HBVfRn4B0bkcsOwtNfhbwauBP4F8Pkk3+i3VRpkUPQoyT+jCYnpqvp+3+3pwVeAryd5F3gS+PUkj/fbpKE7AByoqtmzyV00wTFOfgN4p6oOV9U/Ad8HfqXnNvXlgySXAbTbQz23BzAoepMkNNel36yq/9p3e/pQVXdX1caqmqDpvHy+qsbqm2RV/R3wXpIvtqUbgDd6bFIf9gPXJ1nX/n9xA2PWoT/gGWBbe38b8HSPbfn/1vbdgDH2FeDfAK8m+eu29p+q6n/22Cb143eA6ST/HPg/wG/33J6hqqoXk+wCfkQzGvAVRvSnLJZTkieAXwMuTnIAuBf4NvBUkttpAvS3+mvhcf6EhySpk5eeJEmdDApJUieDQpLUyaCQJHUyKCRJnQwKSVIng0KS1On/ARLmraLH72iZAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x_train,y_train,color='red')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x2809da97790>"
+      ]
+     },
+     "execution_count": 20,
+     "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='red')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "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)\n",
+    "plt.plot(x_test,y_pred,color='blue') # for plotting the regression line\n",
+    "plt.title(\"Linear Regression\")\n",
+    "plt.xlabel('Years of Experience')\n",
+    "plt.ylabel('Salary')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 sq. value: 0.9749154407708353\n"
+     ]
+    }
+   ],
+   "source": [
+    "# For checking how close the data are fitted into the regression line\n",
+    "# R-squared is a goodness-of-fit measure for Linear Regression models\n",
+    "from sklearn import metrics\n",
+    "print('R2 sq. value:',metrics.r2_score(y_test,y_pred))"
+   ]
+  },
+  {
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}