https://github.com/shubhamsoni98/classification-with-random-forest-1

To classify sales into categories (Low, Moderate, High) using Random Forests to inform strategic decisions and optimize marketing strategies.
https://github.com/shubhamsoni98/classification-with-random-forest-1

algorithms anaconda data data-science datacleaning eda jupyter-notebook machine-learning pyhton random-forest scikit-learn visualization

Last synced: about 1 month ago
JSON representation

To classify sales into categories (Low, Moderate, High) using Random Forests to inform strategic decisions and optimize marketing strategies.

• Host: GitHub
• URL: https://github.com/shubhamsoni98/classification-with-random-forest-1
• Owner: shubhamsoni98
• Created: 2024-09-17T08:49:30.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-24T13:20:40.000Z (4 months ago)
• Last Synced: 2024-11-07T12:58:10.734Z (3 months ago)
• Topics: algorithms, anaconda, data, data-science, datacleaning, eda, jupyter-notebook, machine-learning, pyhton, random-forest, scikit-learn, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 315 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Classification-with-Random-Forest-1

To classify sales into categories (Low, Moderate, High) using Random Forests to inform strategic decisions and optimize marketing strategies.

# Sales Prediction with Random Forests

## Objective

To classify sales into categories (Low, Moderate, High) using Random Forests to inform strategic decisions and optimize marketing strategies.

## Solution

### Data Collection

- Utilized the `Company_Data.csv` dataset containing features like CompPrice, Income, Advertising, and categorical variables such as ShelveLoc, Urban, and US.

### Data Preparation

- **Cleaning**: Verified absence of missing values and handled outliers.

- **Encoding**:

  - Applied Ordinal Encoding for `ShelveLoc`.

  - Applied One-Hot Encoding for `Urban` and `US`.

  - Applied Label Encoding for the target variable `Sales`.

### Exploratory Data Analysis (EDA)

- Analyzed feature distributions and their relationships through visualizations such as KDE plots and bar charts.

### Model Building

- Trained a Random Forest Classifier with 500 trees and a maximum depth of 10.

- Evaluated performance using accuracy, classification report, and confusion matrix.

### Execution Time

- 5.31 seconds.

## Business Impact

- **Strategic Insights**: Provided actionable insights for better marketing strategies and resource allocation.

- **Decision Support**: Enhanced understanding of sales drivers and customer segmentation.