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ARCHANA

Data Science Engineer
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Location Ahmedabad
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Member since30+ Days ago
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Contact Details
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Candidate Information
  • User Experience
    Experience 1 Year
  • Cost
    Hourly Rate$5
  • availability
    AvailabilityImmediate
  • work from
    Work FromAny
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    CategoryInformation Technology & Services
  • back in time
    Last Active OnJune 18, 2024
Key Skills
Machine LearningData ScienceDeep Learning
Education
2021-2022

(Data Science )
GTU

2011-2015

(Power Electronics )
GTU

2021-2022

(Data Science)
GTU

Summary

PROJECTS A Real-time Attendance system (In Time-Date, Actual Time, & Late hours) using Computer vision Deep learning face recognition. 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. Text classification model to determine whether Instagram posts are related to pavbhaji or not using RNN LSTM and ML Model. (NLP) 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. End-to-end ML Application (House price Prediction) using Flask and Heroku deployment with all GitHub actions. Perform EDA and create a Linear Regression ML model. In vs code create a Pickle File, HTML, requirement.txt, Procfile, and app.py and cloned on GitHUb. Created Flask Python web framework and the frontend HTML deployed on the Heroku platform. 

Project Details
Title :A Real-time Attendance system (In Time-Date, Actual Time, & Late hours) using Computer vision Deep l
Duration :3
role and responsibileties :

.

Description :

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
role and responsibileties :
  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
Description :

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
role and responsibileties :
  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.

 

Description :

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.


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