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Rossmann Pharmaceuticals: Sales Forecasting and Analysis

Project Overview

This project focuses on building and serving an end-to-end product to forecast sales for Rossmann Pharmaceuticals stores across several cities. The goal is to provide accurate six-week sales predictions, empowering the finance team and store managers with data-driven insights.

The project incorporates analysis of factors such as:

  • Promotions
  • Competitor Activity
  • Weekend and State Holidays

Features

  • Promotion Analysis: Evaluate the effectiveness of promotional campaigns.
  • Competitor Analysis: Assess the impact of competitor proximity and openings on store performance.
  • Customer Behavior Trends: Explore customer activity during weekends, holidays, and store operational hours.
  • Seasonality Insights: Identify patterns in sales based on weekend.

Data Description

The dataset contains the following columns:

  • Date: Daily data for each store.
  • Store: Unique ID for each store.
  • DayOfWeek: The day of the week.
  • Sales: Sales for the store on the given day.
  • Customers: Number of customers for the store on the given day.
  • Promo: Whether a store is running a promotion that day.
  • CompetitionDistance: Distance to the nearest competitor store.
  • Assortment: Assortment type ('a', 'b', 'c').
  • Promo2: Whether the store is part of a continuous promotion.

Methodology

  1. Exploratory Data Analysis (EDA):

    • Analyzed sales trends based on store type, promotions, and competitor activity.
    • Visualized customer behavior trends during weekends and holidays.
  2. Data Preprocessing:

    • Merged multiple datasets (store details, sales data, promotional data).
    • Handled missing values and ensured consistency in time-series data.
  3. Promotion Analysis:

    • Evaluated the sales uplift during promotional periods.
    • Assessed the effectiveness of promotions on customer engagement.
  4. Competitor Impact Analysis:

    • Correlated sales with the proximity and opening of competitors.
    • Investigated the impact of competition on store performance.

Key Results

  • Promotion Effectiveness: Promotions significantly boosted sales, particularly in stores with extended assortments.
  • Competitor Impact: The presence of nearby competitors slightly reduced sales, though city-center stores were less affected.
  • Customer Trends: Weekend sales were normal like other days, with promotions.

Installation

  1. Clone the repository:

    git clone https://github.com/helinatefera/10xWeek4.git  
  2. Navigate to the project directory:

    cd 10xWeek4  

Usage

  1. Promotion Insights: Refer to the notebooks/tasx_1.ipynb notebook for insights into promotional effectiveness.
  2. Competitor Impact: Explore the impact of competitor activity using the notebooks/task_1.ipynb notebook.

Contributing

Contributions are welcome! Please fork the repository, create a feature branch, and submit a pull request.


License

This project is licensed under the MIT License.

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