User Analytics in Telecommunication Industry

A comprehensive User Analytics project focused on analyzing user overview, engagement, experience, and satisfaction in the Telecommunication Industry. The project utilizes data samples provided in the data folder and implements various tools for analysis.

Table of Contents

• Overview
• Installation
• Creating a Virtual Environment
• Usage
• Data Loading
• EDA
• Testing
• Contributing
• License

Installation

1. Clone the repository:

   git clone https://github.com/DanielZerihunGeda/Telecom.git

2. Navigate to the project directory:

   cd Telecom

3. Install the required dependencies:

   pip install -r requirements.txt

be aware that the existing requirements.txt file includes all the packages for the project

If you prefer Conda as your package manager:

1. Open your terminal or command prompt.

2. Navigate to your project directory.

3. Run the following command to create a new Conda environment:

   conda create --name your_env_name python=3.12

   Replace your_env_name with the desired name for your environment e.g. week0 and 3.12 with your preferred Python version.

4. Activate the environment:

   conda activate your_env_name

If you prefer using venv, Python's built-in virtual environment module:

1. Open your terminal or command prompt.

2. Navigate to your project directory.

3. Run the following command to create a new virtual environment:

   python -m venv your_env_name

   Replace your_env_name with the desired name for your environment.

4. Activate the environment:

   • On Windows:

   .\your_env_name\scripts\activate

   • On macOS/Linux:

   source your_env_name/bin/activate

Now, your virtual environment is created and activated. You can install packages and run your Python scripts within this isolated environment. Don't forget to install required packages using pip or conda once the environment is activated.

usage

data-loading

In the src directory, there are two modules: Utility and DataCleaner.

1. Utility Module:

   Takes raw data hosted by PostgreSQL.

   Allows you to assign the following parameters is test.py in tests directory: Username Password Table name Database name

   Used for processing data before conducting Exploratory Data Analysis (EDA).

   The module passes the fetched data and contains necessary methods for cleaning up, including interpolation.

   Returns the processed data as a dataframe.

2. DataCleaner Module:

   Takes the data processed by the Utility module.

   Performs final cleaning steps.

   Returns the fully cleaned data for further EDA analysis.

EDA-analysis

EDA Analysis Directory, you'll find essential functions for Exploratory Data Analysis (EDA). These functions cover various aspects: Univariate Analysis:

Provides tools for analyzing individual variables.

Bivariate Analysis:

Allows the examination of relationships between two variables.

Multivariate Analysis:

Supports the analysis of interactions between multiple variables.

Outlier Analysis:

Identifies and handles outliers within the dataframe. for a given columns