Rahul Choudhary
Bhopal
Summary
Machine learning engineer with proven success in building successful algorithms and predictive models for different industries. Highly adept at clustering and classification, we scrapping, data analysis, and visualization.
A thriving analyst with the ability to apply ML techniques and algorithm development to solve real-world industry problems.
Overview
3
3
years of professional experience
1
1
Certification
Work History
Associate Software Engineer
Accenture
Bangalore
11.2022 - Current
- Developed stored procedures, functions and triggers to support application requirements.
- Created database objects like tables, views, indexes and synonyms in Oracle 11g and 12c databases.
- Optimized existing queries for better performance using SQL query tuning techniques.
- Monitored database performance and troubleshooted issues related to slow running queries or deadlocks.
- Analyzed business requirements and translated them into logical data models.
- Tested changes to ensure that the system works correctly after implementation.
- Analyzed user requirements, procedures and problems to improve system performance.
- Created detailed reports highlighting key insights from the analyzed data.
Data Scientist Consultant
Rubixe
Bengaluru
08.2021 - 04.2022
- Designed a Cost-Effective ticket-raising model using ML
- Developed machine learning algorithms and models to predict customer behavior.
- Implemented supervised learning techniques such as classification, regression, clustering, and decision trees for predictive analytics.
- Performed feature engineering using natural language processing and deep learning frameworks like TensorFlow and Keras.
- Designed experiments to test the performance of various machine learning approaches.
- Built data pipelines to ingest, transform, store and analyze large datasets from multiple sources.
- Utilized unsupervised methods such as anomaly detection and recommendation systems.
- Created dashboards in Tableau to visualize results of machine learning models.
- Optimized hyperparameters of neural networks with grid search or Bayesian optimization techniques.
- Used statistical software to analyze and process large data sets.
Education
Btech - Electrical and Electronics Engineering
Lakshmi Narain College of Technology
05.2022
HIGHER SECONDARY -
G.B.CONVENT HR.SEC.SCHOOL
01.2018
HIGH SCHOOL -
St. Montfort Senior Secondary School
01.2015
Skills
Statistical / Machine Learning
- Statistical analysis : Descriptive / Inferential data analysis , Measure of Central Tendency , Standard Deviation , Variance, probability , Z-Score, Permutation Combination , Removal of outliers, Co-variance, Pearson Correlation, Spearman's Rank Correlation, Hypothesis Testing - Z-Test, T- test, F test , Anova test, Chi-Square Test
- Machine Learning : Data Visualization & EDA , Supervised/ unsupervised Algorithms : Linear, Logistic, Polynomial , Decision Tree/ Decision Tress Regressor & Cross Validation , Bias vs Variance , Assemble Approach , Bagging Boosting, Random Forest, Stacking, Variable Importance , KNN, K-Means, Naive Bayes, SVM , TensorFlow, PyTorch , CNN , NLP Algorithms
Technical Skill
Tools and Language :
- Python, My SQL, Tableau, Flask, Pandas, Numpy, Sciket learn
Key Skills
- Machine learning Model Development, Data Collation and Analysis, Predictive Modeling, System Design & Development, Data Integrity, Model Enhancement, Customer behavior Analysis
Certification
- What is Data Science ? BY IBM, IBM, 02/2021, 03/2021
- HackerRank Python Certificate, HackerRank, 07/2021
- Certified Data Scientist Internship Certificate, 10/19/2021, 04/22/2022
Projects
- Advance House Price Prediction, With 80 explanatory variables describing (almost) every aspect of residential homes, this competition challenges you to predict the final price of each home, Performed: Data Visualization (EDA), Data Preprocessing, Feature Selection, Model Selection and Building, Hyperparameter tuning, Model Evaluation, We trained 8 models, 8 proven to be effective, to Predict House Price Prediction based on information, Linear Regression: 82.76 | Decision Tree Regressor: 69.27 | Random Forest Regressor 87.32 | SVR: -6.01 | ADA Boost: 81.35 | XGBoost: 83.07 EARTHQUAKE DAMAGE PREDICTION
- The Earthquake Damage consists of rows 260601 and 40 features and one class label. There are 3 grades of the damage: 1) represents low damage. 2) represents a medium amount of damage. 3) represents almost complete destruction, Performed: Data Visualization (EDA), Data Preprocessing, Feature Selection, Model Selection and Building Hyperparameter tuning, Model Evaluation, Random Forest Regressor 76.4
Languages
English
First Language
Hindi
Proficient (C2)
C2
Timeline
Associate Software Engineer
Accenture
11.2022 - Current
Data Scientist Consultant
Rubixe
08.2021 - 04.2022
Btech - Electrical and Electronics Engineering
Lakshmi Narain College of Technology
HIGHER SECONDARY -
G.B.CONVENT HR.SEC.SCHOOL
HIGH SCHOOL -
St. Montfort Senior Secondary School
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