Summary

FAQs

• What's your data analysis technique?

  It varies based on the task. For supervised learning tasks, I follow these steps: data cleaning, preprocessing, handling missing values, duplicates, outliers, data transformation, model creation, calculation of feature importance and finally data analysis.

• Which visualization plots do you normally use?

  The choice of visualization plots depends on the type of data being analyzed. I use bar plots for categorical or mixed data, scatter plots for continuous data, line plots for time series analysis, and histograms, Q-Q plots and bell curves for checking data distribution.

• Which softwares do you use for data visualization?

  I use Microsoft Power Bi, Tableau, Excel and Python for data visualization and dashboard creation.

• Which data preprocessing techniques do you use?

  Some data preprocessing techniques I normally use are standardization, scaling, normalization, handling missing values, removing duplicates, identifying outliers, feature engineering, feature extraction, feature selection, & dimensionality reduction.

• Do you perform data transformation?

  Yes, data transformation is a crucial step to ensure reliable analysis in most cases. I employ various advanced techniques, including square root, exponential, yeo-johnson power transformations, and rank transformation, to optimize your data for in-depth analysis.

• Which libraries do you normally use?

  It depends on the specific task. But some of the libraries that I normally use are numpy, pandas, sklearn, matplotlib, seaborn, plotly, tensorflow, keras & pytorch.

• Which machine learning algorithms do you use?

  The choice of algorithm depends on the type of task at hand. I normally use support vector machines, k means clustering, k nearest neighbor, decision tree classifier, random forest classifier, convolutional neural networks, support vector regression & xgboost.

• Which programming languages do you use?

  I use python for data preprocessing, data transformation, model development and data visualization.

• Do you know SQL?

  Yes, I'm well-equipped to handle SQL queries.

• Which IDE do you use?

  I adapt my choice of IDE based on the project's needs. I typically switch between Google Colab, Jupyter Notebook, and Microsoft Visual Studio Code. Google Colab is particularly useful for deep learning tasks, while I prefer using VS Code for data modeling and deploying models.

• Do you provide data science services?

  Yes, I offer various data science services, including classification, regression, clustering, market basket analysis, text classification, text summarization, language translation, sentiment analysis, masked language modeling, next word prediction, image classification, and more.