Course Description:
This course will teach the fundamentals of retail sales forecasting using historical data. Students will learn how to collect, preprocess, and analyze retail sales data, and use various techniques to create predictive models. Students will gain hands-on experience using Python programming language and various data science libraries such as NumPy, Pandas, and Scikit-Learn.
What is Retail Sales Forecasting?
Retail sales forecasting refers to the process of predicting future sales for a retail business based on historical data and other relevant information. The goal of retail sales forecasting is to identify patterns and trends in the historical data that can be used to predict the future demand for products and services, as well as other factors that affect retail sales.
Why should you learn this project?
Learning about retail sales forecasting using historical data is important for the following reasons:
Understand customer behavior: By analyzing sales data, retailers can gain insights into customer behavior and preferences, which can inform product and marketing strategies.
Optimize inventory management: Accurate sales forecasting can help retailers optimize inventory management and avoid overstocking or understocking products.
Improve financial planning: By accurately predicting sales, retailers can plan and budget more effectively, improving their financial performance.
In-demand skill in the job market: Data analysis and predictive modeling skills are in high demand in the job market, particularly in the retail and e-commerce sectors. Learning about retail sales forecasting can help you develop skills that are highly sought after by employers.
Prerequisites:
Basic programming knowledge (preferably Python)
Basic understanding of statistics and linear algebra.
Course Outline:
Introduction to Retail Sales Forecasting
Understanding the concept of retail sales forecasting
Importance of retail sales forecasting
Overview of different methods used for retail sales forecasting
Collecting and Preprocessing Data
Understanding the different sources of retail sales data
Data preprocessing techniques such as data cleaning, data normalization, and data transformation
Techniques for handling missing values and outliers
Exploratory Data Analysis
Understanding the characteristics of retail sales data
Visualizing retail sales data using different plots and graphs
Understanding the correlations between different retail sales variables
Feature Engineering
Understanding the concept of feature engineering
Techniques for selecting relevant features for retail sales forecasting
Handling categorical data using one-hot encoding and label encoding
Predictive Modeling
Understanding the different types of machine learning algorithms for retail sales forecasting
Implementing regression models such as Linear Regression, Random Forest Regression, and Gradient Boosting Regression
Evaluating models using performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared
Time Series Analysis
Understanding the concept of time series analysis
Techniques for analyzing time-series data such as Autocorrelation, Stationarity, and Differencing
Implementing time-series models such as ARIMA and LSTM
Model Deployment
Understanding the deployment of machine learning models for retail sales forecasting
Creating a web-based dashboard using Flask and Plotly
Deploying models on cloud-based platforms such as AWS, GCP, and Azure
Throughout the syllabus, students will use popular data science and machine learning libraries such as NumPy, Pandas, and Scikit-Learn to build and evaluate their retail sales forecasting models. They will also learn how to perform exploratory data analysis, preprocess retail sales data, perform feature engineering, and use time-series analysis to extract valuable information from the data.
The course will cover a range of machine learning techniques, including linear regression, decision trees, random forests, and neural networks, and how to apply these techniques to real-world retail sales data to make accurate predictions about future sales.
Students will also learn how to evaluate the performance of their models using various performance metrics such as mean squared error, mean absolute error, and root mean squared error. By the end of the course, students will
How can Codersarts help in this project?
Consultation: Codersarts can provide expert consultation on your project and offer guidance on best practices for preprocessing text data, model selection, and deployment.
Custom Development: Codersarts can develop custom software solutions for your project, including data preprocessing tools, feature extraction scripts, and machine learning models for toxic comment classification.
Code Review: Codersarts can review your code and offer suggestions for improving efficiency, scalability, and maintainability.
Training: Codersarts can provide online training courses on natural language processing and machine learning to help you and your team develop the skills you need for your project.
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