ARCHANA

Title :A Real-time Attendance system (In Time-Date, Actual Time, & Late hours) using Computer vision Deep l

Duration :3

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Area of Computer vision, Face recognition attendance system which includes person's In Time, Actual Time, and Late Hours with Dates, implemented using Open CV and Face Recognition libraries. Histogram of Oriented Gradients (HOG) and Face landmark Estimation for face detection and deep convolutional neural network is used for face recognition.

Title :Text classification model to determine whether Instagram posts are related to pavbhaji or not using

Duration :-2

1. Project Manager:

   • Define project goals, timelines, and budget.
   • Ensure effective communication and resolve issues.

2. Data Scientist/NLP Engineer:

   • Collect, preprocess, and analyze Instagram post data.
   • Design and implement LSTM-based NLP model for text classification.

3. Machine Learning Engineer:

   • Select and optimize traditional ML models.
   • Evaluate and compare ML model performance.

4. Deep Learning Engineer:

   • Implement and optimize LSTM model architecture.

5. Data Engineer:

   • Set up and maintain data pipeline.

6. QA Engineer:

   • Design and execute model testing.
   • Identify and report issues

The challenge involves using metadata from Instagram posts, provided in a JSON file, Text preprocessing, One hot Representation, Embedding Layer, LSTM layers, Dropout layers for Overfitting. Applied both RNN LSTM and Naive Bayes theorem.

Title :End-to-end ML Application (House price Prediction) using Flask and Heroku deployment with all GitHub

Duration :2

1. Data Scientist/ML Engineer:

   • Conduct Exploratory Data Analysis (EDA).
   • Train and create a Linear Regression ML model.
   • Save the trained model as a Pickle file.

2. Full Stack Developer:

   • Develop the Flask web application using VS Code.
   • Create HTML files for the frontend.

3. DevOps Engineer:

   • Create a requirements.txt file for dependencies.
   • Create a Procfile for Heroku deployment.

4. Git Administrator:

   • Set up a GitHub repository.
   • Push code including HTML, app.py, requirements.txt, and Procfile to GitHub.

5. Heroku Deployment Specialist:

   • Deploy the Flask app on the Heroku platform.

 

Perform EDA and create a Linear Regression ML model. In vs code create a Pickle File, HTML, requirement.txt, Procfile, and app.py and clone on GitHUb. Created Flask Python web framework and the frontend HTML deployed on the Heroku platform.