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Stars1
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LanguageJupyter Notebook
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License
Apache License 2.0 -
Created almost 6 years ago
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Updated over 2 years ago
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Activity-Detection-using-IMU-sensor
User activity detection using IMU (Inertial Measurement Unit) sensors and power of deep learning. The accelerometer data from smart wearables is used for continuous activity detection, which can be used for in depth activity monitoring and recommender systems.Jupyter Notebook
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Predict_RNA-RNA_Interaction
In this project I have used deep learning methods to predict RNA-RNA interaction (RRI) from the sequencing-based training data.Jupyter Notebook
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Manufacturing_Line-i4.0
Shop floor always have challenges. When the part fails at the end of line testing, shop floor manager instinctively retrace your steps to identify at what point you went wrong. Here, the ML based algorithm is closely monitoring the parts as they progress through the manufacturing processes. So that the defective parts can be intelligently identified. An i4.0 use case with machine learning.Jupyter Notebook
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Hierarchical-clustering
Jupyter Notebook
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Correlation_Vapor-Pressure
Jupyter Notebook
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Python-Coding-Interview
Jupyter Notebook
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ANOVA---Sales-Volume
Analysis of VarianceJupyter Notebook
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Logistic-Regression
Jupyter Notebook
3
9
DCNN-timeseries-RUL
DCNN for Machine RUL Prediction using Time-series DataJupyter Notebook
3
10
KNN_K-Nearest-Neighbor
k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regressionJupyter Notebook
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Support-Vector-Regression-SVR-
Jupyter Notebook
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NLP-GloVe-Sentiment_Analysis
Jupyter Notebook
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Hypothesis-Testing---One-sample-t-test
Hypothesis Testing - One sample t testJupyter Notebook
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14
Python
Python learning, assignments, solutionsJupyter Notebook
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Descriptive_Statistics
Descriptive statistics fundamentalsJupyter Notebook
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CNN_MLP-cifar10
Here, we are going to classify images from the CIFAR-10 dataset. This covers preprocessing the imagees, training, validation and prediction using the convolutional neural networks model.Jupyter Notebook
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Chi-Square-Test---Apparel-Company
Chi Square TestJupyter Notebook
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MLR-Case_Study-Housing_Marketing
Jupyter Notebook
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Advanced-Python
Python basics for beginner and advancedJupyter Notebook
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Regression_Analysis
Jupyter Notebook
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21
PCA-Visualization
Jupyter Notebook
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22
Polynomial-Regression
Jupyter Notebook
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23
Naive-Bayes-Classifier
Jupyter Notebook
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Decision-Tree-Regression
Jupyter Notebook
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Simple-Linear-Regression
Simple linear regression is a statistical method that allows us to summarize and study relationships between two continuous (quantitative) variablesJupyter Notebook
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Dummy-Variables
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K-Means-Clustering
Jupyter Notebook
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Multiple-Linear-Regression
Jupyter Notebook
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tflite-rock_paper_scissors
Jupyter Notebook
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Two-sample-t-test
Jupyter Notebook
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31
Random-Forest-Regression
A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=TrueJupyter Notebook
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Web-scraping
Imagine you have to pull a large amount of data from websites and you want to do it as quickly as possible. How would you do it without manually going to each website and getting the data? Well, “Web Scraping” is the answer. Web Scraping just makes this job easier and faster.Jupyter Notebook
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ANN-Churn_Modeling
A Predictive Churn Model is a tool that defines the steps and stages of customer churn, or a customer leaving your service or product. Having a predictive churn model gives you awareness and quantifiable metrics to fight against in your retention efforts.Jupyter Notebook
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Random-Forest-Classification
Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes or mean prediction of the individual trees.Jupyter Notebook
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paired-t-test
Jupyter Notebook
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PROJECT-EDA_Healthcare
The data at hand contains medical costs of people characterized by certain attributes. Explore and generate value out of this data.Jupyter Notebook
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ANN-Bike_Sharing-Boston_Housing
Jupyter Notebook
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Mini-Batch-K-Means-Clustering
Jupyter Notebook
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Python-numpy
Jupyter Notebook
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Market-Basket-Analysis
Jupyter Notebook
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Decision-Tree-Classifier
Jupyter Notebook
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Markdown-Pricing
Jupyter Notebook
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normality-test
Jupyter Notebook
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Time-Series-Analysis-ARIMA
Jupyter Notebook
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Convolutional-Neural-Network-CNN
Jupyter Notebook
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46
Predict_Price_from_Text-NLP-Tensorflow-Case_study
Wide Deep Learning - Wide models are models with sparse feature vectors, or vectors with mostly zero values. Multi-layer deep networks, on the other hand, where there may be unexpected relationships between inputs and outputs. If you have got a prediction task that could benefit from both of these models (recommendation models or models with text inputs are good examples), wide & deep might be a good fit.Jupyter Notebook
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PROJECT---Face-Recognition-and-Blurring
Computer Vision Project: Blurring the face area of people from videos is done in all news channels and to hide the identity of a person. With computer vision, we can automatically detect the face region of the person and use it to blur the image. The project will be useful in blurring the faces of the people in the video.Jupyter Notebook
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Predictive-Maintenace-Case_Study
Failure prediction is a major topic in predictive maintenance in many industries. Observing machine health and condition through sensor data is assumed to facilitate this type of maintenance by predicting Time-To-Failure (TTF) or Remaining Useful Life (RUL) of in-service equipment. This project aim to explore in this area.
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