Step 1: Data Cleaning and Imputation

1. Check Dataset Structure: Load the dataset (ObesityDataSet_raw_and_data_sinthetic.csv), examine its structure, and identify any missing or incorrect values.

2. Impute Missing Values: For columns with missing values, impute using the mean of that column.

Step 2: Exploratory Data Analysis (EDA)

1. Height and Weight Distribution:

   • Visualize the distribution of heights and weights.

2. Top 10 Ages with Highest Weight:

   • Identify and visualize the top 10 ages with the highest average weight.

3. Distribution of CAEC values:

   • Explore and visualize the distribution of CAEC (Consumption of Alcoholic Drinks) values.

4. Average Ages with Family History of Overweight:

   • Calculate and visualize the average ages of individuals with and without a family history of overweight.

5. Correlation Matrix:

   • Construct a correlation matrix to understand the relationships between different variables.

Step 3: Data Preprocessing

1. Label Encoding: Apply label encoding to categorical columns.
2. Standard Scaling: Apply standard scaling to continuous columns.
3. One-Hot Encoding: Perform one-hot encoding on categorical columns that require it.
4. Train-Test Split: Split the dataset into training and testing sets for model evaluation.

Step 4: Model Selection

• Determine whether predicting NObeyesdad is a regression or classification problem.
• Choose appropriate algorithms based on the problem type (e.g., classification algorithms like Logistic Regression, Decision Trees, Random Forest, etc.).

Step 5: Model Evaluation

By following these steps, your GitHub repository will provide a clear and structured overview of your project, making it easier for others to understand and replicate your analysis.