- Constructed a Logistic regression model to predict if the user will click on and ad or not.
- Constructed histograms, jointplots and pairplots for feature understanding.
- Used Standard Scaler to fit transform the data.
- Achieved an accuracy score of 96%. Provided the confusion matrix and classification report.
- Libraries used: Sci-kitlearn, Matplotlib, Pandas, Numpy, Seaborn
- Constructed a kNN classification model to predict the species of a flower.
- Constructed stripplots to visualize the correlation between the attributes of a flower.
- Analysed attributes using jointplots and histplots.
- Using the elbow method, determined the optimal 'K' value for the model.
- Achieved an accuracy score of 97%. Calculated the accuracy score, classification report and confusion matrix.
- Libraries used: Scikit-learn, Pandas, Numpy, Matplotlib, Seaborn
- Analyzing for the average app rating on the app market using a histogram.
- Constructed joint plots for “app price vs rating” and “app size vs rating”.
- Exploration of ‘app price vs category’ using stripplots.
- Box plot analysis of sentiment polarity for paid vs. free apps
- Libraries used: Pandas, Numpy, Matplotlib, Seaborn
- Visualized Nobel Prize winners from 1901-2016 using Seaborn library to create plots such as lmplot, scatterplot, lineplot, etc.
- Libraries used: Pandas, Numpy, Matplotlib, Seaborn
React
Typescript
Django
Laravel
Facebook
Microsoft
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Alibaba
D3
Tencent