Data Analysis Using Python and R

Aim

The aim of these projects was to make predictions using various machine learning algorithms and choose from the analysis the best model that gave the least error.

Supervised Machine Learning was deployed using the Regression Method

Steps taken in making an informed decision on the project are as follows:

1. Data Pre-processing.
2. Data Visualization.
3. Missing value Analysis.
4. Outlier Analysis.
5. Feature Selection.

• Correlation analysis.

6. Feature Scaling.

• Normalization.

7. Splitting into Train and Test Dataset.
8. Hyperparameter Optimization.
9. Model Development I. Linear Regression II. Support Vector Regression III. ARIMA
   IV. Random Forest
10. Model Performance.