(CAE) Data pilots

What it does

It analyses the data with all the features and makes a future prediction for the same.

How we built it

1. Cleaning the data by removing all the blank values and NAN values.
2. Filtering out the data with Z score Analysis which helps to remove the outliers and noise.
3. Compare the results with respect to the prediction Algorithm. -. Decision Tree
   • K Nearest Neighbour
   • Random Forest
   • Neural Network(Keras)

Challenges we ran into

• Cleaning the data and also analyzing the data by figuring out the outliers.
• Figuring out the perfect model with respect to the given set.

Accomplishments that we're proud of

• Higher Accuracy with neural networks

What we learned

• Different ways to analyze the data and get the final proper dataset with the minimalistic error.
• Applying different techniques which helped us to get the in-depth knowledge of some of the Algorithms.

What's next for CAE Data analysis

• Trying out a combination of different models which helps to increase the precision.

Built With

• keras
• python
• scikit-learn
• seaborn