SayakGiri

Bankruptcy_Prediction

Classification-of-Images-of-Clothing

Classification of Images of Clothing, viz. T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle Boot using Neural Networks.

Classification-Subscription_to_a_term_deposit

Predicting if a client will subscribe to a term deposit of a retail banking institution based on age of the client, their job type, their education level, their marital status etc. using Decision Tree algorithm.

Classification_using_Keras

Predicting if a female has breast cancer using data on clump thickness, uniformity of cell size, uniformity of cell shape, marginal adhesion, single epithelial cell size, bare nuclei, bland chromatin, normal nucleoli and mitosis.

Concrete_Strength_Prediction

Using Linear Regression to predict the concrete strength in csMPa using 8 features.

Covid-19--Visualization-Modelling_and_SImulation

Analyzing the spread of Coronavirus and using SIR model for prediction.

Covid-19_Visualization

Statistical analysis of Covid-19 and studying its spread graphically using matplotlib.

Credit_Risk_Analyser

This project aims at building a classification model to predict whether a person is going to be a defaulter or not based on various parameters like age, income , education, marital status etc. This helps the banks to perform credit risk analysis i.e. possibility of the borrower's repayment failure and the loss caused to the financer when the borrower does not repay the contractual loan obligations.

Extract-locations-form-a-text-using-NLP-

People, places, and things—nouns—play a crucial role in language, conveying the sentence’s subject and often its object. Due to their importance, it’s often useful when processing text to try to identify nouns and use them in applications. This is known as either entity identification or named-entity recognition (NER). Entity Recognition is used in almost all application of NLP. As an example, suppose you have a list of reviews on mobile phones. Using NLP, you can extract different entities like company name, mobile phone model, price, place etc.

SVM-Visualization

Support Vector Machines (SVM), among classifiers, are probably the most intuitive and elegant, especially for binary classification tasks. We are going to visualize SVM for the case of linearly separable as well as non-linearly separable data.

SayakGiri/Credit_Risk_Analyser

This project aims at building a classification model to predict whether a person is going to be a defaulter or not based on various parameters like age, income , education, marital status etc. This helps the banks to perform credit risk analysis i.e. possibility of the borrower's repayment failure and the loss caused to the financer when the borrower does not repay the contractual loan obligations.

SayakGiri/Extract-locations-form-a-text-using-NLP-

People, places, and things—nouns—play a crucial role in language, conveying the sentence’s subject and often its object. Due to their importance, it’s often useful when processing text to try to identify nouns and use them in applications. This is known as either entity identification or named-entity recognition (NER). Entity Recognition is used in almost all application of NLP. As an example, suppose you have a list of reviews on mobile phones. Using NLP, you can extract different entities like company name, mobile phone model, price, place etc.

SayakGiri/SVM-Visualization

Support Vector Machines (SVM), among classifiers, are probably the most intuitive and elegant, especially for binary classification tasks. We are going to visualize SVM for the case of linearly separable as well as non-linearly separable data.

SayakGiri/Bankruptcy_Prediction

SayakGiri/Classification-of-Images-of-Clothing

Classification of Images of Clothing, viz. T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle Boot using Neural Networks.

SayakGiri/Classification-Subscription_to_a_term_deposit

Predicting if a client will subscribe to a term deposit of a retail banking institution based on age of the client, their job type, their education level, their marital status etc. using Decision Tree algorithm.

SayakGiri/Classification_using_Keras

Predicting if a female has breast cancer using data on clump thickness, uniformity of cell size, uniformity of cell shape, marginal adhesion, single epithelial cell size, bare nuclei, bland chromatin, normal nucleoli and mitosis.

SayakGiri/Concrete_Strength_Prediction

Using Linear Regression to predict the concrete strength in csMPa using 8 features.

SayakGiri/Covid-19--Visualization-Modelling_and_SImulation

Analyzing the spread of Coronavirus and using SIR model for prediction.

SayakGiri/Covid-19_Visualization

Statistical analysis of Covid-19 and studying its spread graphically using matplotlib.

SayakGiri/Detect_the_presence_of_Parkinson-s_Disease_in_individuals

To build a model to accurately detect the presence of Parkinson’s disease in an individual using XGBoost algorithm.

SayakGiri/Fake_News_Detection

Using Python to build a model that can classify whether a piece of news is REAL or FAKE using TfidfVectorizer and PassiveAggressiveClassifier.

SayakGiri/Hierarchical_clustering

Hierarchical clustering of Iris data set.

SayakGiri/Intellify-Random-

SayakGiri/Intellify-Random_Forest-

Using Random Forest Algorithm for regression and classification.

SayakGiri/Lab-Area_plots_Histograms_and_Bar_charts

SayakGiri/Lab-Generating_maps_in_Python

SayakGiri/Lab-Introduction_to_Matplotlib_and_Line_Plots

SayakGiri/Lab-Pie_charts_Box_plots_Scatter_plots_and_Bubble_plots

SayakGiri/PCA-on-IRIS-dataset

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. The resulting vectors (each being a linear combination of the variables and containing n observations) are an uncorrelated orthogonal basis set. PCA is sensitive to the relative scaling of the original variables.

SayakGiri/Recommendation-System-New

City recommendation system using Python

SayakGiri/Recommendation_System

SayakGiri/Regression_using_Keras

Predicting the csMPa using cement, slag, flyash, water, superplasticizer, coarseaggregate, fineaggregate, and age.

SayakGiri/Sentiment_Analyser

To build a custom sentiment analyser that works on movie reviews and effectively categorises sentiments around them

SayakGiri/Twitter_Bot_Classification

SayakGiri/Waffle_charts_Word_clouds_and_Regression_plots