• Using Libraries: Pandas, NumPy, SciPy, shapely, sklearn, cv2, Tensorflow, Keras
• Dstl provides you with 1km x 1km satellite images in both 3-band and 16-band formats. The goal is to detect and classify the types of objects found in these regions.
• Every object class is described in the form of Polygons and MultiPolygons, which are simply a list of polygons. We provide two different formats for these shapes: GeoJson and WKT. These are both open source formats for geospatial shapes.
• Apply U-Net architecture to solve the image segmentation problem.

• Using libraries: Pandas, NumPy, SciPy, Matplotlib, sklearn, sci-kit-learn, Model Selection
• we can more accurately identify new ways to solve our most common challenge, binary classification problems such as: is a customer satisfied? Will a customer buy this product? Can a customer pay this loan?
• Data cleaning, visualization and creating data pipeline for modeling.
• Using LightGBM and Catboost Model.

• Using libraries: Pandas, NumPy, SciPy, Matplotlib, sklearn, sci-kit-learn, Tensorflow, Keras, Talos
• This project is to build a model that predicts human activities such as Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing or Laying.
• There are a total of 561 features and doing EDA on datasets.
• Logistic regression or Linear SVM or RBF SVM and LSTM(1 layer or 2 layers) given the best accuracy.

Identify which questions asked on Quora are duplicates of questions that have already been asked. Task is predicting whether a pair of questions are duplicates or not. Apply some machine learning model e.g. Logistics Regression, Linear SVM and XGBoost.

Using Amazon Fine Food Reviews data set apply BoW, tf-idf, W2V, tf-idf W2V. Analysis data set with Machine Learning Models. Apply LSTM model to predict reviews sentiment.