Assignment36

• Linear Regression on random data
  

  I've generated some random data and want to fit a line on it.
  First generated some random line ,then calculate the correct slope to fit the line on it ,
  and after that I use the linregress method from scipy library to fit the generated line on the data
  

As it's shown below there's a bit different between the line calculated its slope and
the line fitted calculated by the linregress method

• Linear regression on Boston Housing dataset
  

  To predict the price of Boston houses ,I selected two features,
  Age and the number of rooms ,then plot them with the price predicted on a 3D surface
  The cost function used here is Linear Least Square (LLS)

Assignment37

• Digikala:Data Analysis

  Working on digikala dataset to get some Information

  • Monthly Order Amount

    

  • Customer Per City

    

• Webcam QRcode scanner

  This is a program to scan the Qrcode readen from webcam
  First install pyzbar( pip install pyzbar ) , then run the code

• Linear Least Sqaure on Abalone dataset

  Fit a line on two linear correlated features (Length and Height)

  

  MAE loss	0.039
  MSE loss	0.002
  Huber loss	0.0012
  Hinge loss	0.97

Assignment38

• Regression on Boston dataset using Perceptron algorithm

  To predict the price of Boston houses , I used perceptron algorithm as a regressor
  Two most correlated features with price,ZN and RM are selected
  

  • Feature correlation with price

    

  • Error Training Curve

    

  • Fitted plane on test data