Multiple Linear Regression
Assignment Task: Your task is to perform a multiple linear regression analysis to predict the price of Toyota corolla based on the given attributes. Dataset Description: The dataset consists of the following variables: Age: Age in years KM: Accumulated Kilometers on odometer FuelType: Fuel Type (Petrol, Diesel, CNG) HP: Horse Power Automatic: Automatic ( (Yes=1, No=0) CC: Cylinder Volume in cubic centimeters Doors: Number of doors Weight: Weight in Kilograms Quarterly_Tax: Price: Offer Price in EUROs Taskes: 1.Perform exploratory data analysis (EDA) to gain insights into the dataset. Provide visualizations and summary statistics of the variables. Pre process the data to apply the MLR. 2.Split the dataset into training and testing sets (e.g., 80% training, 20% testing). 3.Build a multiple linear regression model using the training dataset. Interpret the coefficients of the model. Build minimum of 3 different models. 4.Evaluate the performance of the model using appropriate evaluation metrics on the testing dataset. 5.Apply Lasso and Ridge methods on the model.
Interview Questions: 1.What is Normalization & Standardization and how is it helpful? 2.What techniques can be used to address multicollinearity in multiple linear regression?
Ensure to properly comment your code and provide explanations for your analysis. Include any assumptions made during the analysis and discuss their implications.