From ecb9137a12444e1b8e9d8c2c1b81555a35e327e5 Mon Sep 17 00:00:00 2001
From: wengJJ <31797645+wengJJ@users.noreply.github.com>
Date: Mon, 6 Aug 2018 09:05:37 +0800
Subject: [PATCH] Add files via upload

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+# Simple Linear Regression
+
+
+<p align="center">
+  <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%202.jpg">
+</p>
+
+
+# Step 1: Data Preprocessing
+```python
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+dataset = pd.read_csv('studentscores.csv')
+X = dataset.iloc[ : ,   : 1 ].values
+Y = dataset.iloc[ : , 1 ].values
+
+from sklearn.cross_validation import train_test_split
+X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0) 
+```
+
+# Step 2: Fitting Simple Linear Regression Model to the training set
+ ```python
+ from sklearn.linear_model import LinearRegression
+ regressor = LinearRegression()
+ regressor = regressor.fit(X_train, Y_train)
+ ```
+ # Step 3: Predecting the Result
+ ```python
+ Y_pred = regressor.predict(X_test)
+ ```
+ 
+ # Step 4: Visualization 
+ ## Visualising the Training results
+ ```python
+ plt.scatter(X_train , Y_train, color = 'red')
+ plt.plot(X_train , regressor.predict(X_train), color ='blue')
+ ```
+ ## Visualizing the test results
+ ```python
+ plt.scatter(X_test , Y_test, color = 'red')
+ plt.plot(X_test , regressor.predict(X_test), color ='blue')
+ ```