California Housing Price Prediction:

This California Census Data is taken from the US Census Bureau, which has 20,640 records and 10 different variables:  Longitude  Latitude  Housing median age  Total rooms  Total bedrooms  Population  Households  Median income  Ocean proximity  Median house value

This project has been completed in three different parts as given below:

(1) Data Wrangling: • Checking missing values • Filling missing values • Creating new features • Converting categorical variables into numerical • Standardizing data • Checking multicollenearity

(2) EDA (Exploratory Data Analysis): • Distribution of households with different visualizations • Heat Map and other visualizations to see the correlations between variables. • Checking Outliers with boxplots and scatter plots • Detecting and Removing Outliers • Checking Skewness of features

(3) Machine Learning Algorithms to train data:

• Linear Regression • Ridge Regression • Support Vector Machine Regression • Decision Tree Regression • Random Forest Regression • Gradient Boosting Regression • XGBoost Regression