Completed the tutorial

Author: Bhanu Mittal

README.md

@@ -33,7 +33,7 @@ python3 --version
 which python3
 ```
 
-The ouput of the directory should be somethhing like "/usr/local/bin/python3.7" or "/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7"
+The ouput of the directory should be something like "/usr/local/bin/python3.7" or "/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7"
 
 ## Getting Started
 
@@ -55,7 +55,86 @@ if __name__ == '__main__':
 
 
 ## Solving our first ML Problem
-We are gonna work on our first dataset by using simple [linear regression](http://onlinestatbook.com/2/regression/intro.html). We would be using a fairly small dataset for our problem called [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg).
+We are gonna work on our first dataset by using simple [linear regression](http://onlinestatbook.com/2/regression/intro.html). We would be using a fairly small dataset for our problem called [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg). Before we get stared we need to install some libraries in our Python environment. In order to do that, click on the terminal tab in PyCharm at the bottom of the window, it should have (venv) as its set environment. Run the following commands in it.
+```bash
+pip install pandas
+pip install sklearn
+pip install matplotlib
+```
+Our dataset is present in the file "auto-mpg.data", so download it inside your project directory.
+
+### Preparing the Dataset
+First let's see how can we explore the dataset in Python. Observe that the values of each row in the dataset are seperated by spaces which are not fixed in length. Thus it would require us to read the file line by line in Python. Below is the source code to do the needfull.
+```python
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import sklearn
+from sklearn.linear_model import LinearRegression
+
+headers = ['cylinders','displacement','horsepower','weight'
+           ,'acceleration','model year','origin']
+mpg = [] #Dependent variable : mpg (miles per gallon)
+features = [] #Independent variables: rest of them except car name
+
+#Preparing the data
+raw = open('auto-mpg.data')
+for line in raw:
+    i=0
+    var = []
+    for y in line.split():
+        if (i==0):  # The first column is mpg
+            mpg.append([float(y)])
+        if (i<8 and i>0):  # Not including the last column i.e. "car names"
+            if(y=='?'): # Handaling missing values for "horsepower" 
+                y = 0   # (Setting them to 0) Later we will change them
+            if(i==4):
+                var.append(float(y.replace('.','')))
+            else:
+                var.append(float(y))
+        i+=1
+    features.append(var)
+    
+ 
+df_x = pd.DataFrame.from_records(features, columns = headers)
+df_y = pd.DataFrame.from_records(mpg, columns = ['mpg'])
+
+```
+
+Now we have the entire datset in the variables: '*df_x*' and '*df_y*'. You can preview these variables and get an overall summary of them by using:
+```python
+print(df_x.describe(include='all'))
+print(df_y.describe(include='all'))
+print(df_x.head()) # Prints the top 5 values
+print(df_y.head()) # Prints the top 5 values
+```
+Now let us change the missing values, i.e. now 0, to their average values.
+```python
+avg_bhp = (np.average(df_x['horsepower'])*len(df_x))/(len(df_x)-6)
+df_x['horsepower'] = df_x['horsepower'].replace(0,avg_bhp)
+```
+Now let's train our first ML model in Python.
+```python
+X_train, X_test, Y_train, Y_test = sklearn.model_selection.train_test_split(
+        df_x,df_y,test_size=0.2,random_state = 5)
+lm = LinearRegression()
+
+lm.fit(X_train, Y_train)
+pred_test = lm.predict(X_test)
+
+print('coefficients :(all vars)')
+print(lm.coef_)
 
+print('Intercept :(all vars)')
+print(lm.intercept_)
+
+print('Accuracy Score(all vars): %s' % lm.score(X_test,Y_test))
+
+residues = (Y_test - pred_test)
+
+plt.scatter(X_test, Y_test,  color='black') # The actual values
+plt.plot(X_test, pred_test, color='blue', linewidth=3) # The predicted values
+```
+Voila! You have successfully completed your first ML model.
 ## License
 This guide is free to use for non-commercial purposes. For any improvements please reach out to me on: bhanu93(dot)iitd(at)gmail(dot)com.