Problem

A retail company was struggling to accurately forecast their sales, which was leading to ineffective decision-making and a lack of understanding of sales trends. They needed a solution that would allow them to predict future sales with a high degree of accuracy.

Our team was brought in to develop a predictive model that would utilize demographic and sales data to forecast retail sales.

Learn more about our approach here.

Solution

We developed a Random Forest Regressor model using Python libraries including pandas, NumPy, scikit-learn, and Matplotlib. The model utilized demographic and sales data to predict future sales with a high degree of accuracy.

In order to prepare the data for modeling, we preprocessed it by handling missing values, encoding categorical variables, and standardizing numerical features. This allowed us to create an accurate model that could be used for effective sales trend analysis and decision-making.

Impact

Achieved 93% accuracy in sales predictions
Provided the retail company with the ability to accurately forecast future sales
Enabled effective decision-making based on accurate sales trend analysis

"This project had an incredible impact on our ability to understand our sales trends and make informed decisions. We are extremely grateful for the work done by this team."

Team

Rushikesh Choudhari - Data Scientist
- Developed the Random Forest Regressor model using Python libraries including pandas, NumPy, scikit-learn, and Matplotlib
- Preprocessed data by handling missing values, encoding categorical variables, and standardizing numerical features
Rushikesh Choudhari - Business Analyst
- Analyzed sales trends and provided insights for effective decision-making based on accurate sales predictions
- Worked closely with the retail company to ensure that the model met their specific needs and requirements

Retail Sales Forecasting with 93% Accuracy