2nd_Hand_Car_Price_Prediction

Algorithm: -

1) Import the relevant libraries

2) Preprocess the Raw Data:-

• i. Descriptive Stats [all variables]
• ii. Variables of Interest
• iii. Missing Values
• iv. Checking for Outliers [PDF] and reset index
• v. OLS Assumptions [independent <- dependent]
• vi. Relax Assumptions [log independent <- dependent]
• vii. Multi-Collinearity Check [independent (high is worst)]
• viii.Rearrange the Data [dependent ——— independent]
• ix. Dummy Categorical [relax 1st variable]

3) Parallel Linear Regression Model:-

• i. Declare Targets and Inputs
• ii. Standardize Inputs
• iii. Train_Test_Spilt
• iv. Create the regression
• v. Weights and Bias df [+Weight ~ +target]
• vi. Feature Selection (F-regression)
• vii. Plot and Compare targets and its prediction IP [y_hat <- prediction]
• viii.Plot and Compare using Residual [y_train - y_hat]

4) Testing the Data:-

• i. Reseting the testing index
• ii. Predicting the Inputs test-set
• iii. Plot and Compare targets and its prediction IP [y_hat <- prediction]
• iv. Plot and Compare using Residual [y_test - y_hat]
• v. Prediction’s Dataset Performance