View the total put up right here: https://www.linkedin.com/posts/akash-halder-nil_machinelearning-datascience-python-activity-7283155994996600832-Zd5-?utm_source=share&utm_medium=member_desktop
💡Key Takeaways:
- Understood the right way to seamlessly combine backend logic with a dynamic frontend.
Venture 2: Information Evaluation on Unemployment Fee in India
This mission centered on understanding the unemployment state of affairs in India by way of information evaluation and visualization. Right here’s what I completed:
- Information Evaluation and Insights:
- Cleaned and processed unemployment information to make sure accuracy.
- Carried out in-depth EDA to uncover tendencies and patterns, corresponding to regional unemployment disparities and seasonality.
2. Visualization with Energy BI:
- Created an interactive real-time dashboard utilizing Microsoft Energy BI.
- The dashboard displayed metrics like unemployment charges over time, comparisons throughout states, and key insights by way of intuitive charts and graphs.
- Leveraged Energy BI’s information refresh capabilities for real-time updates.
💡Key Takeaways:
View the total put up right here: https://www.linkedin.com/posts/akash-halder-nil_machinelearning-datascience-python-activity-7283155994996600832-Zd5-?utm_source=share&utm_medium=member_desktop
💡Key Takeaways:
- Understood the importance of storytelling by way of information for decision-making.
Venture 3: Electronic mail Spam Detection
The third mission was a sensible implementation of pure language processing (NLP) and machine studying to resolve a real-world downside — spam detection. Right here’s what I labored on:
- Mannequin Coaching:
- Preprocessed textual content information utilizing the NLTK library by making use of methods like tokenization, stopword elimination, and stemming.
- Educated a Multinomial Naive Bayes (NB) mannequin, which carried out greatest resulting from its precision in dealing with textual content information.
2. Mannequin Analysis:
- Used k-fold cross-validation to make sure strong analysis and generalization.
- Optimized the mannequin by way of hyperparameter tuning.
3. Net App with Streamlit:
- Constructed an intuitive net utility utilizing Streamlit that permits customers to enter messages and classify them as spam or not spam.
- Designed a easy and interactive UI with visible suggestions and real-time predictions.
💡Key Takeaways:
View the total put up: https://www.linkedin.com/posts/akash-halder-nil_datascienceintern-eda-powerbi-activity-7283656373148536833-DuqE?utm_source=share&utm_medium=member_desktop
💡Key Takeaways:
- Found how frameworks like Streamlit simplify ML mannequin deployment.
This internship was a transformative expertise, permitting me to:
- Discover end-to-end workflows, from information cleansing to mannequin deployment.
- Achieve hands-on expertise with instruments like Flask, Streamlit, and Energy BI.
- Perceive the worth of presenting information and fashions in an accessible and user-friendly method.
Every mission taught me one thing distinctive:
- The Iris Flower Classification mission honed my expertise in API growth and frontend integration.
- The Unemployment Evaluation mission deepened my understanding of information visualization and storytelling.
- The Spam Detection mission emphasised the nuances of NLP and the significance of user-centric design in net apps.
I’m grateful to Oasis Infobyte for this chance and for serving to me develop as a budding information scientist. Should you’re trying to break into Information Science, I extremely advocate diving into comparable initiatives to construct your expertise and confidence.
- Machine Studying
- Pure Language Processing (NLP)
- Information Visualization (Energy BI, Matplotlib, Seaborn)
- Deployment (Flask, Streamlit)
- API Improvement and Integration
Embarking on this internship journey with Oasis Infobyte has been a very transformative expertise. It strengthened my ardour for information science and supplied me with the boldness to sort out real-world issues utilizing know-how.
Whether or not you’re simply beginning out or are deep into your information science journey, do not forget that each mission you tackle is a chance to study, develop, and showcase your expertise.
💬 Have questions on these initiatives or interested in the right way to get began in Information Science? Be at liberty to achieve out or share your ideas within the feedback! I’d love to attach with like-minded people and assist in any method I can.
