🧠 Machine-Learning-Notes

🙋‍ Welcome to my ML repos. These are the lecture notes and exercises I have done when I took the some of the ML course from 365 Data Science and Coursera.

📖 Table of Contents

• 1. Used Car Price Prediction
• 2. US Hose Price Prediction
• 3. Gradient Descent

1. Used Car Price Prediction 🚗

🎯 Predict the price of different used cars using Multiple Linear Regression Model

👉 View My Notebook

🛠️ Preprocessing

• Expore the descriptive statistics of the variables
• Remove/ Input Missing Values
• Probability Density Function of each variable of interest
• Remove oulier/ abnormal value
• Checking Ordinary Least Square assumption (Apply Log Transformation to make distribution of a variable more normally distributed; Apply Variance Inflation Factor to check Multicollinearity
• Creat dummy variables

🚀 Build & Evalutate Model Performace

• Declare the inputs and targets
• Feature scalling (Standardization)
• Train Test split
• Create a Regression model
• Check performance (Using the Residuals PDF and R-squared)

2. US House Price Prediction 🏡

🎯 Predict the Price of the US House using Multiple Linear Regression Model

⚙️ I used same techniques as I did in the Used Car Price Prediction.

👉 View My Notebook

3. Gradient Descent 🧠

🎯 Build the Gradient Descent Algorithm from Scratch!

👉 View My Notebook