https://github.com/adityajn105/kagglesolvedproblem
Data Science problem on Kaggle Solved by me. Keep improving Accuracy.
https://github.com/adityajn105/kagglesolvedproblem
kaggle kaggle-competition numpy pandas python scikit-learn tensorflow
Last synced: 10 months ago
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Data Science problem on Kaggle Solved by me. Keep improving Accuracy.
- Host: GitHub
- URL: https://github.com/adityajn105/kagglesolvedproblem
- Owner: adityajn105
- Created: 2018-01-07T13:54:14.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2019-05-14T15:31:14.000Z (over 6 years ago)
- Last Synced: 2025-03-27T02:39:20.193Z (10 months ago)
- Topics: kaggle, kaggle-competition, numpy, pandas, python, scikit-learn, tensorflow
- Language: Jupyter Notebook
- Size: 43.5 MB
- Stars: 2
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# This is repository to store solutions to data science problem solved by me given on Kaggle.
## Problems
* `*` - To be Improved Later
* `ACC` - Accuracy / 100 (For Classification Problems)
* `MAE` - Mean Absolute Error / 100 (For Regression Problems)
Index | Kaggle Link | Status | Tags
------------- | ------------- | ------------- | -------------
1 | [Titanic: Machine Learning from Disaster ](https://www.kaggle.com/c/titanic) | ACC: 0.80382 | Data Cleaning, Feature Engineering
2 | [Bike Sharing Demand](https://www.kaggle.com/c/bike-sharing-demand) | MAE: 0.50357 | Seaborn, Feature Eng, Keras ANN
3 | [Twitter sentiment analysis](https://www.kaggle.com/c/twitter-sentiment-analysis2) | *ACC: 0.72608 | NLTK, textual classification
4 | [Digit Recognizer](https://www.kaggle.com/c/digit-recognizer) | ACC: 0.99042 | Keras CNN, Image Classification
5 | [What's Cooking](https://www.kaggle.com/c/whats-cooking-kernels-only/overview) | *ACC: 0.697497 |
6 | [House Prices: Advanced Regression Techniques](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) | *MAE: 0.18217 | Catboost, Data Cleaning
7 | [Forest Cover Type Prediction](https://www.kaggle.com/c/forest-cover-type-prediction) | ACC: 0.74479 |
8 | [Sentiment Analysis on Movie Reviews](http://www.kaggle.com/c/sentiment-analysis-on-movie-reviews) | ACC: 0.59882 | NLTK, CountVect, TFIDF
9 | [Data Science London + Scikit-learn](https://www.kaggle.com/c/data-science-london-scikit-learn) | ACC: 0.91281 |
10 | [Ghouls, Goblins, and Ghosts... Boo!](https://www.kaggle.com/c/ghouls-goblins-and-ghosts-boo) | ACC: 0.73534 |
11 | [Amazon.com - Employee Access Challenge](https://www.kaggle.com/c/amazon-employee-access-challenge/) | ACC: 0.67644 |
12 | [New York City Taxi Trip Duration](https://www.kaggle.com/c/nyc-taxi-trip-duration) | Not Done |
13 | [Bag of Words Meets Bags of Popcorn](https://www.kaggle.com/c/word2vec-nlp-tutorial) | *ACC: 81.964 | Word2Vec, Gensim, NLP
14 | [New York City Taxi Fare Prediction](https://www.kaggle.com/c/new-york-city-taxi-fare-prediction) | Not Done |
15 | [Random Acts of Pizza](https://www.kaggle.com/c/random-acts-of-pizza) | Not Done |
16 | [Quora Question Pairs](https://www.kaggle.com/c/quora-question-pairs/) | Not Done | Topic Similarity, NLP
17 | [Predict Future Sales](https://www.kaggle.com/c/competitive-data-science-predict-future-sales) | Not Done |
18 | [Facial Keypoints Detection](https://www.kaggle.com/c/facial-keypoints-detection) | Not Done |
19 | [Facial Expression Recognition Challenge](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/) | Not Done |
20 | [West Nile Virus Prediction](https://www.kaggle.com/c/predict-west-nile-virus) | Not Done |
21 | [Dogs vs. Cats](https://www.kaggle.com/c/dogs-vs-cats) | ACC: 78.257 | Keras CNN, Image Classification
22 | [Human Activity Recognition with Smartphones](https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones/) | *ACC: 0.9619 | MultiClass Classification
23 | [Aerial Cactus Identification](https://www.kaggle.com/c/aerial-cactus-identification/) | *ACC: 0.9989 | Keras CNN, Image Classification
24 | - | Not Done |
25 | - | Not Done |
26 | - | Not Done |