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About Me
- Hands on experience in Base R programming, Python.
- Strong background in Machine Learning, NLP, Algorithm writing.
- Good command on statistical techniques like Regression, Classification, Clustering, Natural Langua...
- Strong working experience in SQL Server
- Experience in entire data science life cycle, i.e., source =>clean=>explore=>communicate.
- Strong experience in converting unstructured data into structured and workable datasets.
- Expertise in data modeling techniques and data exploration techniques using various statistical tools.
- Strong analytics experience into statistical requirements like recommendation & optimization.
- Adapt and adhere to industry standards while working with multi-cultural teams.
- Worked directly with clients and other stakeholders to understand their business need and design methodology, execute analysis, interpret results and generate key insights.
- Experience in executing the project end to end right from deriving the business case to sharing the key business insights to clients after the analysis.
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Skills
Positions
Portfolio Projects
Description
1. Applied the hierarchical clustering and plotted the dendrogram.
2. Identified the 3 cluster- High spending, medium spending and Low spending - with help of dendrogram
3. compared the results with k-means clustering.
4. By making use of this data, company can announce various offers to various segments
5. Appliedhierarchical clustering , Agglomerative Clustering, Kmeans clustering, Python, Pnadas, numpy
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Wrote data pre-processing scripts
- Normalization: is to clean data to obtain better features
- converting all letters to lower or upper case
- removing numbers
- removing punctuations
- removing stop words, sparse terms, and particular / rare words
- spelling correction
- stemming: removal of suffices,
- Lemmatization: converts the word into its root word, rather than just stripping the suffices.
2. Worked on Feature extraction by Tf-idf, CountVectorizer, Singular Vector Decomposition (SVD) and Feature Engineering
3. This is developed using Python, NLP and Machine Learning like Naïve Bays, XGboost.
Description
1. Worked on data gathering, data cleaning, data modelling,
2. Worked on explorative data analysis
3. Outlier and missing value detection and tratement
4.Data Imbalance
5. Features Selection
6. Building Classification Model
7. Evaluating Accuracy and other metrics
8. Parameter Tuning
9. Finalize the Model5.
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