Ajaksolution

Table of Contents • Customer Churn Prediction • Image Classification using Convolutional Neural Network (CNN) • Retail Predictive Analytics • Student Performance Analysis • Installation • Technologies Used • How to Run • Contact

Customer Churn Prediction Objective The goal of this project is to predict customer churn (whether a customer will leave the service or not) using machine learning techniques. By analyzing historical customer data, the model identifies patterns that indicate churn behavior, allowing businesses to take action in retaining their customers. Dataset The dataset includes customer demographics, usage patterns, contract information, and whether they have churned. You can download the dataset from here. Features • Customer data preprocessing including handling missing values, encoding categorical variables, and scaling numerical data. • Modeling: Use machine learning models like logistic regression, decision trees, random forest, and gradient boosting to predict churn. • Evaluation: Model evaluation through accuracy, precision, recall, and ROC-AUC score.

Image Classification using Convolutional Neural Network (CNN) Objective This project aims to classify images into different categories using a deep learning model, specifically a Convolutional Neural Network (CNN). CNNs are highly effective for image classification tasks because they can capture spatial hierarchies in images. Dataset The dataset used for this project is the CIFAR-10 dataset, which contains 60,000 images across 10 different classes. You can download the dataset directly using the TensorFlow/Keras library. Features • Preprocessing: Normalization of images, data augmentation for better model generalization. • CNN Architecture: Design and train a CNN model consisting of convolutional layers, pooling layers, dropout layers, and fully connected layers. • Model Optimization: Use optimizers like Adam and learning rate schedules to improve performance. • Evaluation: Evaluate the model using accuracy, precision, recall, and confusion matrix.

Retail Predictive Analytics Objective This project focuses on predicting customer behavior in retail, such as purchase patterns, product preferences, and future trends. Predictive analytics can help retailers optimize stock, increase sales, and improve customer satisfaction. Dataset The dataset consists of historical retail data that includes product sales, customer demographics, and transactional data. You can find suitable datasets from here. Features • Exploratory Data Analysis (EDA): Analyze sales trends, customer segments, and key business metrics. • Time Series Forecasting: Use models like ARIMA, SARIMA, or machine learning models such as XGBoost or LSTM for predicting future sales. • Clustering: Segment customers based on their purchase behavior using clustering techniques such as K-Means. • Evaluation: Evaluate the models using RMSE, MAE, and other forecasting accuracy metrics.

Student Performance Analysis Objective This project aims to analyze and predict student performance based on various factors like demographics, parental education, study time, and other personal attributes. By understanding the data, educators can identify areas where students might need additional support. Dataset The dataset contains information about students’ test scores in various subjects, along with background information. You can use datasets like the one from here. Features • Data Preprocessing: Clean and preprocess the dataset by handling missing values, encoding categorical features, and scaling numeric features. • Exploratory Data Analysis (EDA): Analyze relationships between student performance and various factors like parental education, study time, and gender. • Modeling: Train machine learning models (e.g., decision trees, random forest, XGBoost) to predict student scores. • Evaluation: Use metrics such as accuracy, F1-score, and R² to evaluate model performance. Prerequisites • Python 3.10 Installing Dependencies To install the required dependencies, run: bash Copy code pip install -r requirements.txt

Technologies Used • Python: Core programming language. • Pandas, NumPy: Data manipulation and numerical operations. • Scikit-Learn: For traditional machine learning algorithms. • TensorFlow/Keras: For deep learning models (CNN). • Matplotlib, Seaborn: For data visualization. • Jupyter Notebook: Used for development and experimentation.

  1. Clone the repository: bash Copy code git clone https://github.com/your-username/your-repo-name.git cd your-repo-name
  2. Install dependencies: bash Copy code pip install -r requirements.txt
  3. Running the Projects: • Customer Churn Prediction: Open the Jupyter notebook or Python script churn_prediction.ipynb and run the cells. • Image Classification (CNN): Run the notebook image_classification_cnn.ipynb to train and evaluate the CNN model. • Retail Predictive Analytics: Run the notebook retail_predictive_analytics.ipynb to analyze retail data and perform forecasting. • Student Performance Analysis: Run the notebook student_performance_analysis.ipynb to explore student data and build prediction models.

Contact For any questions or feedback, feel free to reach out:

• Email: your.ajakmakuach@gmail.com • GitHub: https://github.com/Ajakdeng/Ajaksolution/tree/main