I will do machine learning projects and prepare data

Preprocessing :

1. Extract Data From Resources.
2. Missing data fill.
3. Data Analysis.
4. Data Distributions.
5. Data Visualization.
6. Data Covariance and Correlation.
7. Dimensionality Reduction.

Data :

1. Classification.
2. Regression.
3. Clustering.

Machine Learning :

1. Supervised and Unsupervised Learning.
2. Decision Trees (ex : XGBoost).
3. Recommender Systems.
4. Real-Time Systems.

Predictive :

1. Linear Regression.
2. Polynomial Regression.
3. Multiple Regression.
4. Multi-Level Models.

Techniques :

1. Prevent overfitting.
2. Detecting outliers.
3. Handling Unbalanced Data Oversampling.
4. Feature Engineering.
5. Handle Missing Data.
6. Binning, Transforming, Encoding, Scaling, and Shuffling.