Monday – October 2,2023.

I have written this draft report .

Data Preparation:

1. Data Gathering: Collect data from various sources.
2. Data Cleaning: Remove duplicates, handle missing values, and correct inconsistencies.
3. Data Integration: Combine three Excel sheets into a dataset containing 354 data points. Column Naming: Rename columns for clarity and understanding.

Exploratory Data Analysis (EDA):

1. Summary Statistics: Compute mean, median, skewness, kurtosis, standard deviation, and percentiles.
2. Data Visualization: Generate plots and charts to visualize data and explore relationships between variables.
3. Outlier Detection: Identify and handle outliers.
4. Geographical Analysis: It was discovered that 138 counties in the dataset belong to a single state. Tattnall County in Georgia has the highest combined percentage of inactivity, obesity, and diabetes, totaling 47.3%.

Data Modeling:

1. Algorithm Selection: Choose appropriate machine learning or statistical algorithms based on the problem type (classification, regression, clustering, etc.).
2. Model Evaluation: Assess model performance using evaluation metrics such as accuracy, F1-score, and RMSE on the testing data.
3. Hyperparameter Tuning: Optimize model hyperparameters to enhance performance.

Interpretation of Model:

1. Feature Interpretation: Determine which features have the most significant impact on the model’s predictions.Model Explanation: Understand the rationale behind the model’s predictions.

Reporting and Visualization:

1. Report Creation: Summarize findings, insights, and model performance in clear and concise reports. Result Visualization: Use charts, graphs, and dashboards to communicate results effectively.

Deployment & Real-world Monitoring:

1. Model Deployment: To obtain answers, implement the model in a real-world environment.Continuous Monitoring: Monitor the model’s performance in the real world and make necessary adjustments.

Documentation:

1. Process Documentation: Document all the steps taken during the analysis for future reference.

Feedback:

1. Feedback Collection: Gather input from professors and teaching assistants to improve the analysis and presentation.