This project focuses on analyzing an insurance dataset to gain insights through statistical methods and machine learning techniques. The primary objectives are:
-Data Cleaning: Handling missing values, outliers, and normalizing data.
-Exploratory Data Analysis (EDA): Summary statistics and visualizations.
-Statistical Modeling: Linear regression models to predict insurance claims.
-Principal Component Analysis (PCA): Dimensionality reduction for better feature interpretation.
-Machine Learning Models: Applying predictive models for classification and regression tasks.
-Handling Outliers: Replacing extreme values based on interquartile range (IQR).
-Normalization: Standardizing numeric variables for better model performance.
-Feature Engineering: Creating meaningful variables for prediction.
-Linear Regression: Predicting insurance claims based on customer data.
-PCA: Identifying key factors in the dataset.
-Machine Learning Models: Evaluating different models for better accuracy.