ikoghoemmanuell/Tunisian-Fraud-Detection

# Tunisian Fraud Detection Increase sales of groceries using exploratory data analysis and machine learning. ## Introduction This repository serves as a case study for the Grocery Store Forecasting Challenge for Azubian on Zindi Africa. The challenge focuses on predicting the sales of various products in grocery stores based on historical data. This case study explores the data, methodologies, and models used to tackle the challenge, providing insights into the predictive analytics process. ## Dataset The dataset used for this case study is provided on the Zindi Africa platform. It consists of historical sales data, product information, and store information. The dataset is utilized to build models that can accurately forecast future sales and help grocery stores optimize their inventory management and supply chain operations. ## Setup Fork this repository and run the notebook on Colab. Learn about Google Colab here. Learn how to connect Colab to your github account here. ## Methodology 1. Exploratory Data Analysis (EDA): The case study begins with an in-depth exploration of the dataset to understand its structure, variables, and patterns. EDA techniques such as data visualization and statistical analysis are applied to gain insights into the sales patterns and relationships between variables. 2. Feature Engineering: The dataset is preprocessed and transformed to create meaningful features that capture relevant information for sales forecasting. This involves tasks such as handling missing values, encoding categorical variables, and creating lagged features to account for time dependencies. 3. Model Development: Various machine learning and time series forecasting models are developed and evaluated to identify the best-performing approach. This may include traditional regression models, ensemble methods, or advanced techniques specifically designed for time series forecasting, such as ARIMA, SARIMA, or Prophet. 4. Model Evaluation: The performanc …