Updated Doc

Exploratory_Data_Analysis.ipynb

@@ -18,7 +18,45 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 1. Enviroment Preparation"
+    "<img src=\"banner.jpg\" alt=\"banner\" style=\"width:1500px;height:300px;\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Table\n",
+    "- [1. Environment Setup](#1.-Environment-Setup)\n",
+    "- [2. Data Exploration](#2.-Data-Exploration)\n",
+    "- [3. Data Analysis](#3.-Data-Analysis)\n",
+    "- [4. Data Processing](#4.-Data-Processing)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Notebook Overview\n",
+    "This Notebook is focused on exploring & analysing the Dataset.\n",
+    "\n",
+    "### Dataset\n",
+    "\n",
+    "- **Source**: Loan_Data\n",
+    "- **Initial Dataset Size**: 10K entries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Environment Setup"
    ]
   },
   {
@@ -77948,7 +77986,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4. Data Preprocessing"
+    "## 4. Data Processing"
    ]
   },
   {
@@ -78224,13 +78262,6 @@
    "source": [
     "df.to_csv(\"Data/Processed_Loan_Data.csv\", index=False)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
   }
  ],
  "metadata": {

Loan_Default_Prediction_Models.ipynb

@@ -14,49 +14,37 @@
     "---"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"banner.jpg\" alt=\"banner\" style=\"width:1500px;height:300px;\">"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Content Table\n",
-    "- [0. Environment Setup](#0.-Environment-Setup)\n",
-    "- [1. Data Exploration & Management](#1.-Data-Exploration-&-Management)\n",
-    "- [2. Feature Selection](#2.Feature-Selection)\n",
-    "  - [A. Quantitative Data](#A.-Quantitative-Data)\n",
-    "  - [B. Qualitative Data](#B.-Qualitative-Data)\n",
-    "- [3. Final Data Preparation](#3.-Final-Data-Preparation)\n",
-    "- [4. Statistical Modeling](#4.-Statistical-Modeling)\n",
-    "  - [A. Multicollinearity tests](#A.-Multicollinearity-tests)\n",
-    "  - [B. Data Standardization](#B.-Data-Standardization)\n",
-    "  - [C. Model creation & fitting](#C.-Model-creation-&-fitting)\n",
-    "  - [D. Model Optimization](#D.-Model-Optimization)\n",
-    "  - [E. Model testing](#E.-Model-testing)\n",
-    "- [5. Conclusion](#5.-Conclusion)"
+    "- [1. Environment Setup](#1.-Environment-Setup)\n",
+    "- [2. Data Preprocessing](#2.-Data-Preprocessing)\n",
+    "- [3. Model Experimentation](#3.-Model-Experimentation)\n",
+    "  - [A. Logistic Regression](#A.-Logistic-Regression)\n",
+    "  - [B. Random Forest](#B.-Random-Forest)\n",
+    "- [4. Conclusion](#4.Conclusion)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Project Overview\n",
-    "\n",
-    "This project aims to analyze the determinants for a woman being the primary source of revenue in a household using data from the \"Conditions de Travail 2013\" survey. The analysis will be focused on individuals living in couples in the same household.\n",
-    "\n",
-    "### Objectives\n",
-    "The objective of the project is to analyze, based on the 2013 Working Conditions survey, the determinants for a woman being the primary source of revenue in the household.\n",
-    "1. Data Exploration & Management: Explore the dataset and the available data and handle missing data.\n",
-    "2. Feature Selection: The dataset presents 541 Columns/Features. A selection needs to be made to be able to conduct further analysis.\n",
-    "3. Build and evaluate a logistic regression model to identify significant predictors of a women being the primary source of revenue in the household.\n",
+    "### Notebook Overview\n",
+    "This Notebook is focused on Model implementation, training & evaluation; All while using Mlflow to log parameters, metrics and artifacts\n",
     "\n",
     "### Dataset\n",
     "\n",
-    "- **Source**: Conditions de Travail 2013 (Volet \"Actif\")\n",
-    "- **Initial Dataset Size**: 33,673 individuals\n",
-    "- **Filtered Dataset**: Individuals living in couples at the same household\n",
-    "\n",
-    "### Dependencies\n",
-    "\n",
-    "- Python libraries: pandas, numpy, matplotlib, seaborn, statsmodels, scikit-learn, plotly, scipy"
+    "- **Source**: Loan_Data\n",
+    "- **Initial Dataset Size**: 10K entries"
    ]
   },
   {
@@ -70,7 +58,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 1. Environment Prepration"
+    "## 1. Environment Setup"
    ]
   },
   {

README.md

@@ -64,23 +64,20 @@ The main objective of this project is build an image and a pipeline with all the
 To pull the Docker image from Docker Hub, run the following command:
 ```sh
 # Pull the docker image
-$ docker pull medkallel/dunhumby-the-complete-journey-dashboard:latest
-
-# Or if you downloaded the .tar image
-$ docker load -i dunhumby-the-complete-journey-dashboard.tar
+$ docker pull medkallel/loan-default-prediction:latest
 ```
 #### 2. Building the Docker Image
 If you prefer to build the Docker image locally, navigate to the project directory and run:
 
 ```sh
 # Build the docker image
-$ docker build -t dunhumby-the-complete-journey-dashboard .
+$ docker build -t loan-default-prediction .
 ```
 #### 3. Running the Docker Container
 To run the Docker container, use the following command:
 ```sh
 # Run the docker container
-$ docker run -p 8501:8501 dunhumby-the-complete-journey-dashboard
+$ docker run -p 5000:5000 Loan-Default-Prediction
 ```
 > [!TIP] 
 > You can access the app on another device by following the link: ```http://<server-ip>:8501```
@@ -95,9 +92,9 @@ To run this project locally, follow these steps:
 1. Clone the repository:
 ```sh
 # Clone the repository
-$ git clone https://github.com/Medkallel/Dunhumby-The-complete-Journey-Dashboard
+$ git clone https://github.com/Medkallel/Loan-Default-Prediction.git
 # Navigate into the directory
-$ cd Dunhumby-The-complete-Journey-Dashboard
+$ cd Loan-Default-Prediction
 ```
 2. Install the required dependencies:
 ```sh
@@ -109,7 +106,7 @@ $ pip install -r requirements.txt
 
 1. To use the App, Just run the app.py
 ```sh
-python app.py
+$ python app.py
 ```
 ---
 ## Project structure