https://github.com/iguptashubham/customer-intent-prediction

Logistic regression is a binary classification technique used to predict outcomes like customer churn or purchase intent. It models the probability of an event happening (e.g., a customer making a purchase) based on input features.
https://github.com/iguptashubham/customer-intent-prediction

data-science data-science-projects logistic-regression machine-learning machine-learning-projects machinelearning project sklearn

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Logistic regression is a binary classification technique used to predict outcomes like customer churn or purchase intent. It models the probability of an event happening (e.g., a customer making a purchase) based on input features.

• Host: GitHub
• URL: https://github.com/iguptashubham/customer-intent-prediction
• Owner: iguptashubham
• Created: 2024-06-21T08:18:20.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-07-21T12:45:59.000Z (4 months ago)
• Last Synced: 2024-07-21T14:11:48.851Z (4 months ago)
• Topics: data-science, data-science-projects, logistic-regression, machine-learning, machine-learning-projects, machinelearning, project, sklearn
• Language: Jupyter Notebook
• Homepage: https://customer-intent-prediction.streamlit.app/
• Size: 139 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Customer-intent-prediction

Logistic regression is a binary classification technique used to predict outcomes like customer churn or purchase intent. It models the probability of an event happening (e.g., a customer making a purchase) based on input features.

# Pipeline of Model

Certainly! Let's delve into the model pipeline I created. This pipeline combines several essential steps to build a predictive model:

![Screenshot 2024-06-21 175623](https://github.com/iguptashubham/customer-intent-prediction/assets/140319219/360d04bd-3619-47df-bb6e-4ee368e5e999)

1. **Data Preprocessing**:

   - The pipeline begins by handling raw data. It performs transformations to make the data suitable for modeling.

   - Specifically, it:

     - **One-Hot Encodes** categorical features (like product category and brand) to convert them into numerical representations.

     - **Standardizes** numerical features (such as customer age, purchase frequency, and satisfaction) to have zero mean and unit variance.

     - Applies a **Power Transformation** (Box-Cox) to the product price feature to improve its distribution.

2. **Model Selection and Training**:

   - After preprocessing, the pipeline feeds the transformed data into a **Logistic Regression** model.

   - Logistic Regression is a binary classification algorithm that predicts the probability of an event (e.g., customer making a purchase).

   - The model learns from historical data to make predictions based on the input features.

3. **Hyperparameter Tuning**:

   - Although not explicitly mentioned, hyperparameters (like solver, maximum iterations, and regularization) can be fine-tuned to optimize model performance.

   - The choice of hyperparameters affects the model's accuracy and generalization ability.

4. **Model Evaluation**:

   - It's crucial to assess how well the model performs. Metrics like accuracy, precision, recall, or F1-score help evaluate its effectiveness.

   - Iteratively refine the model based on evaluation results.

5. **Deployment**:

   - Once satisfied with the model's performance, deploy it in real-world scenarios (e.g., an application or website).

   - Continuously monitor and maintain the model as new data becomes available.

In summary, this pipeline transforms data, trains a logistic regression model, and prepares it for practical use. 🚀