Pratiksha Pratap More

Developed key skills in data analysis and machine learning within collaborative and fast-paced environment. Demonstrated ability to extract actionable insights from complex datasets and present findings to stakeholders. Seeking to transition these transferrable skills into new field to drive data-informed decisions.

• Front-End Developer Course
• Internship at IIT Kanpur
• Accenture North America: Data Analysis Visualization Simulation
• Career Essentials in Data Analysis by Microsoft/LinkedIn

1) Fake Reviews Detection Using  Machine Learning:

• Collected and preprocessed datasets of online reviews from platforms like Amazon and Yelp, including text data and reviewer metadata.
• Performed feature engineering using techniques like TF-IDF, sentiment analysis, and behavioral analysis of reviewer patterns.
• Built predictive models using machine learning algorithms such as Logistic Regression, Random Forest, and Gradient Boosting, achieving [mention accuracy, e.g., 90%].
• Leveraged Natural Language Processing (NLP) tools for text analysis and classification tasks.
• Evaluated model performance using metrics like precision, recall, F1 score, and ROC-AUC.
• Addressed class imbalance using SMOTE and other resampling techniques.
• Deployed and tested the model to identify fake reviews in real-time scenarios.

Technologies Used:
Python, Scikit-learn, TensorFlow, Pandas, NLTK,  Jupyter Notebook.

2)Analyse Adult Census Population of United States.

• Cleaned And Preprocessed Data.
• Conducted EDA Using Pandas And Seaborn.
• Built Classification Models .