diff --git a/Linear Regression.ipynb b/Linear Regression.ipynb
new file mode 100644
index 0000000..0571eb9
--- /dev/null
+++ b/Linear Regression.ipynb	
@@ -0,0 +1,426 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset=pd.read_csv(\"Salary_Data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=dataset.iloc[:,:-1].values\n",
+    "y=dataset.iloc[:,1].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 39343.,  46205.,  37731.,  43525.,  39891.,  56642.,  60150.,\n",
+       "        54445.,  64445.,  57189.,  63218.,  55794.,  56957.,  57081.,\n",
+       "        61111.,  67938.,  66029.,  83088.,  81363.,  93940.,  91738.,\n",
+       "        98273., 101302., 113812., 109431., 105582., 116969., 112635.,\n",
+       "       122391., 121872.])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.1],\n",
+       "       [ 1.3],\n",
+       "       [ 1.5],\n",
+       "       [ 2. ],\n",
+       "       [ 2.2],\n",
+       "       [ 2.9],\n",
+       "       [ 3. ],\n",
+       "       [ 3.2],\n",
+       "       [ 3.2],\n",
+       "       [ 3.7],\n",
+       "       [ 3.9],\n",
+       "       [ 4. ],\n",
+       "       [ 4. ],\n",
+       "       [ 4.1],\n",
+       "       [ 4.5],\n",
+       "       [ 4.9],\n",
+       "       [ 5.1],\n",
+       "       [ 5.3],\n",
+       "       [ 5.9],\n",
+       "       [ 6. ],\n",
+       "       [ 6.8],\n",
+       "       [ 7.1],\n",
+       "       [ 7.9],\n",
+       "       [ 8.2],\n",
+       "       [ 8.7],\n",
+       "       [ 9. ],\n",
+       "       [ 9.5],\n",
+       "       [ 9.6],\n",
+       "       [10.3],\n",
+       "       [10.5]])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "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": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2.9],\n",
+       "       [ 5.1],\n",
+       "       [ 3.2],\n",
+       "       [ 4.5],\n",
+       "       [ 8.2],\n",
+       "       [ 6.8],\n",
+       "       [ 1.3],\n",
+       "       [10.5],\n",
+       "       [ 3. ],\n",
+       "       [ 2.2],\n",
+       "       [ 5.9],\n",
+       "       [ 6. ],\n",
+       "       [ 3.7],\n",
+       "       [ 3.2],\n",
+       "       [ 9. ],\n",
+       "       [ 2. ],\n",
+       "       [ 1.1],\n",
+       "       [ 7.1],\n",
+       "       [ 4.9],\n",
+       "       [ 4. ]])"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.5],\n",
+       "       [10.3],\n",
+       "       [ 4.1],\n",
+       "       [ 3.9],\n",
+       "       [ 9.5],\n",
+       "       [ 8.7],\n",
+       "       [ 9.6],\n",
+       "       [ 4. ],\n",
+       "       [ 5.3],\n",
+       "       [ 7.9]])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "regressor=LinearRegression()\n",
+    "regressor.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred=regressor.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 40835.10590871, 123079.39940819,  65134.55626083,  63265.36777221,\n",
+       "       115602.64545369, 108125.8914992 , 116537.23969801,  64199.96201652,\n",
+       "        76349.68719258, 100649.1375447 ])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 37731., 122391.,  57081.,  63218., 116969., 109431., 112635.,\n",
+       "        55794.,  83088., 101302.])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "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_train,y_train,color=\"red\")\n",
+    "plt.plot(x_train,regressor.predict(x_train),color=\"blue\")\n",
+    "plt.title(\"Salary vs Experience(TrainSet)\")\n",
+    "plt.xlabel('Years of Experience')\n",
+    "plt.ylabel('Salary')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "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=\"red\")\n",
+    "plt.plot(x_train,regressor.predict(x_train),color=\"blue\")\n",
+    "plt.title(\"Salary vs Experience(TestSet)\")\n",
+    "plt.xlabel('Years of Experience')\n",
+    "plt.ylabel('Salary')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": "3.8.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Salary_Data.csv b/Salary_Data.csv
new file mode 100644
index 0000000..a6863aa
--- /dev/null
+++ b/Salary_Data.csv
@@ -0,0 +1,31 @@
+YearsExperience,Salary
+1.1,39343.00
+1.3,46205.00
+1.5,37731.00
+2.0,43525.00
+2.2,39891.00
+2.9,56642.00
+3.0,60150.00
+3.2,54445.00
+3.2,64445.00
+3.7,57189.00
+3.9,63218.00
+4.0,55794.00
+4.0,56957.00
+4.1,57081.00
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