+1 (415) 644 5123
+44 1253 335595
+91 9875 492266
Arjun kumar
Hyderabad
,
India
Machine Learning Engineer
24000 USD / Year
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Notice Period: Days
3 Years
About Me
Languages & Frameworks: Python, R, SQL, Tableau.
Packages: Scikit-Learn, NumPy, SciPy, Pandas, Matplotlib, Seaborn.
...
Statistics/ML: Linear/Logistic Regression, SVM, Random Forests, Naïve Bayes, KNN, Clustering.
Deep Learning: CNN, RNN, LSTM.
Deep Learning Frameworks: Open CV, TensorFlow, Keras
NLP: Text Mining, Sentiment Analysis, Latent Semantic Analysis (LSA), LDA, Word2vec
Cloud: Google Cloud Platform (Big Query, Big table, Data flow, Data Prep, Data Proc, Data Lab)
Amazon Web Services (Redshift, Athena, Glue, S3, DynamoDB, EC2, Lambda, EMR)
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Skills
Positions
Portfolio Projects
Description
- Project belongs to E-commerce domain where I have worked on Machine learning with Python libraries for the below scenarios.
- Build a model for Repurchase Return Product scenario, which will identify the new customers (or) returning customers who are returning the product and buying the same product and it will predict the they are returning the product wontedly (or) genuinely based on their past data.
Description
- Worked towards improve the efficiency of the model that Engage and Personalize for Consumers.
- Worked to understand the business priorities over the different kind of predicators to influence the model efficiency and to make better pricing decisions.
- Worked with different kind of classification techniques to analyze the sentimental analysis of the customer and how far to Enhance Inventory and Store Management.
- Analysis of customer segmentation for different kind of predicator factors.
- Help to predicate using Machine Learning Techniques by using almost 20 to 30 factors.
- Used Classification model to detect the feasibility of getting product sold or not.
- Customer feedback about the experience and usage of the products and services by using NLP.
Description
- Project belongs to E-commerce domain where I have worked on Machine learning with Python libraries for the below scenarios.
- Build a Collaborative filtering (Frequently bought together) in Recommendation engine, which will recommend other products based on the what currently they bought.
- Build a Content based filtering (Similar products) in Recommendation engine, which will recommend products based on similarity between products.
- Collaborative filtering model which will take the events, item properties, product categories as the input variables and recommend other products based on the what currently they bought.
- Content based filtering model which will take product id, product description as the input and recommend the similar products.
Verifications
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Profile Verified
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Preferred Language
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English - Fluent
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Hindi - Fluent
Available Timezones
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