/datascience

My projects and practices on various segments of machine learning and deep learning.

Primary LanguageJupyter Notebook

Data Science Projects Index

# Project Subject Category Description
1 Insurance-Data-Analysis.ipynb Applied Statistics Machine Learning To dive deep into insurance data, to check if we can find out some valuable insights.
2 Loan_Buying_Prediction.ipynb Supervised Learning Machine Learning The bank management wants to explore ways of converting its liability customers to personal loan customers. So our objective is to dive into and analyse the data and predict the likelihood of a liability customer buying the personal loans.
3 Silhouette_Modeling_PCA.ipynb Unsupervised Learning Machine Learning The purpose is to classify a given silhouette as one of three types of vehicle, using a set of features extracted from the silhouette. The vehicle may be viewed from one of many different angles.
4 SUBSCRIPTION_TERM_DEPOSITE_PREDICTION.ipynb Ensembled Technique Machine Learning Our objective is to dive into and analyse the data and predict the likelihood if the client will subscribe (yes/no) a term deposit (variable y).
5 ConcreteStrength_Modeling.ipynb Featurization, Model Tuning Machine Learning The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly superplasticizer, coarse aggregate, and fine aggregate. So the purpose is to create models using the data to observe the strength of high performance concrete using Machine Learning.
6 Recommendation_model.ipynb Recommendation System Machine Learning Build a recommendation system to recommend products to customers based on the their previous ratings for other products.
7 Image_Clasification-SVHN_NN.ipynb Nueral Network Deep Learning The objective of the project is to learn how to implement a simple image classification pipeline based on a deep neural network.
8 NLP_Sarcasm_Detection.ipynb NLP Deep Learning Sarcasm Detection using Hybrid Neural Network.
9 Face_recognition_CV.ipynb ComputerVision Deep Learning In this problem, we use a pre-trained model trained on Face recognition to recognize similar faces. Here, we are particularly interested in recognizing whether two given faces are of the same person or not.
10 Face_detection_CV.ipynb ComputerVision Deep Learning Task is to predict the boundaries(mask) around the face in a given image.
11 Pnuemonia_detection.ipynb ComputerVision Deep Learning Our mission is to build a pneumonia detection system, to locate the position of inflammation in an image. Tissues with sparse material, such as lungs which are full of air, do not absorb the X-rays and appear black in the image. Dense tissues such as bones absorb X-rays and appear white in the image. While we are theoretically detecting “lung opacities”, there are lung opacities that are not pneumonia related. REPORT: Final_Report.pdf