3. Abstract
Sales forecasting is pivotal in ensuring businesses can efficiently manage their supply
chains and resources. This report outlines a project in which time series forecasting
techniques, enhanced by machine learning algorithms, were applied to predict future
sales. Specifically, the XGBoost model was employed to predict future sales data
values by using historical trends, lag features, and rolling statistics. This model
enables businesses to improve resource planning and respond more effectively to
fluctuating market demands. Evaluation of the model using RMSE demonstrated a
high level of accuracy.
CSE, GST, Visakhapatnam
4. Introduction
Time series forecasting is critical to data science, particularly when predicting values
such as sales or demand over time. Traditional forecasting methods, such as moving
averages or ARIMA models, can often struggle with real-world data's complexities and
non-linear relationships. In recent years, machine learning models like XGBoost have
emerged as more powerful tools for time series forecasting, providing greater
accuracy by capturing intricate patterns in the data. This report focuses on developing
a machine learning-based forecasting model to predict future sales based on past
performance, enabling businesses to better plan inventory, manage supply chains,
and improve overall decision-making.
CSE, GST, Visakhapatnam
5. Techniques/Methodology/Software
The techniques used in this project involve:
•Data Preprocessing: Handling missing values and cleaning the dataset.
•Feature Engineering: Constructing new features from the original data, such as lag features
(previous sales values), rolling window statistics, and time-based features (day of the week,
month, year).
•Model Selection: XGBoost was selected due to its ability to model non-linear relationships
and handle a wide variety of data features.
•Model Training and Testing: The model was trained on historical sales data, and testing was
performed using time-based cross-validation to avoid data leakage.
•Software Used: Python, Jupyter Notebooks, Pandas, NumPy, XGBoost, Scikit-learn, and
Matplotlib for visualizing the results.
CSE, GST, Visakhapatnam
6. Results
The application of the XGBoost model for time series forecasting provided highly
accurate results in predicting future sales figures. The model's performance was
evaluated using the Root Mean Square Error (RMSE), which is a standard metric for
measuring forecasting accuracy. The trained model exhibited a low RMSE, indicating a
close alignment between the predicted and actual values. Additionally, by analyzing
the feature importance scores from XGBoost, it was found that lagged sales data and
day-of-the-week variables contributed the most to the model’s predictive capability.
This suggests that past sales behavior, coupled with seasonal trends, plays a
significant role in determining future sales performance.
CSE, GST, Visakhapatnam
7. Problem Statement
For Commerce applications
Problem 1:
Predicting Monthly Sales
for Retail Stores
Description: The task is to forecast the monthly sales for retail
stores based on historical data, seasonal trends, and
promotional activities to optimize inventory management and
reduce stockouts.
CSE, GST, Visakhapatnam
8. Problem Statement
For Commerce applications
Problem 2: Demand
Forecasting for E-
commerce Products
Description: The goal is to predict the future demand for
products sold online, using past sales data, customer behavior
patterns, and marketing campaigns to enhance supply chain
efficiency and meet customer expectations.
CSE, GST, Visakhapatnam
