Prasad P.

Prasad P.

Software Engineer at Visteon | AI Enthusiastic | Machine Learning | Deep Learning | NLP

Belagavi , India

Experience: 2 Years

Prasad

Belagavi , India

Software Engineer at Visteon | AI Enthusiastic | Machine Learning | Deep Learning | NLP

18683.8 USD / Year

  • Notice Period: 90 Days

2 Years

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About Me

Working Professional with 2.8 years of experience. Have gained good exposure to understand various aspects of Artificial Intelligence through the Masters Program which has developed a great sense of confidence at a very personal level. Hence, I s...

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Portfolio Projects

Store Sales Prediction (Retail)

Company

Store Sales Prediction (Retail)

Description

  • Transformed the variables by using data manipulation techniques like One-Hot Encoding and performed an Exploratory Data Analysis to see the impact of variables over Sales.

  • Used ML models such as Linear-Regression, Lasso, XGRegressor. verified model performance along with techniques such as PCA, GridSearchCV for hyper-parameter tuning.

  • Time-Series analysis/prediction with traditional ML model ARIMA and also LSTM.

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Pet Classification Model Using CNN

Company

Pet Classification Model Using CNN

Description

  • Built Deep Learning CNN (Convolutional Neural Network) model to classify Cats and Dogs.

  • Validated data augmentation techniques to overcome the challenge of fewer data images (20 images of each category) and there was an improvement in the model performance

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Home Security : Face Recognition Model

Company

Home Security : Face Recognition Model

Description

Implemented Object detection model using the jetson-interface package, deployed on NVIDIA Jetson-Nano board.

• Face recognition model training and deployment on the Jetson-Nano/raspberry-pi 4

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Mercedes-Benz Greener Manufacturing

Company

Mercedes-Benz Greener Manufacturing

Description

  • Regression Problem, prediction of target variable in seconds time that the car needs to pass the test
  • Performed EDA, label Encoding, PCA for dimensionality reduction

  • Developed XGBoostRegressor model.

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Income Qualification

Company

Income Qualification

Description

  • Classification Problem, Identification of the level of income qualification needed for the families in Latin America
  •  Able to achieve accuracy of 94.27% with RandomForestClassifier

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Twitter Combat Hate Speech

Company

Twitter Combat Hate Speech

Description

  • Developed NLP pipeline for text casing, cleaning, tokenizing using NLTK.
  • Used GridSearchCV and KFold for optimal parameters of LogisticRegression.

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Skills

Python Sklearn