skshiraj Goto Github PK

cnn-on-mnist

cobol

data-science--cheat-sheet

Cheat Sheets

data-science-tutorials

Data Science Tutorials

datascience-projects

decision-tree

decision-tree-classifier

decision-tree-donor-choose

Apply Decision Trees on Donors Choose dataset

decision-tree-on-donors-choose-dataset

Applying Decision Tree on Donors Choose Dataset

deeplearning

desision-trees-with-various-hyperparametrs

Apply both Random Forrest and GBDT on these feature sets Set 1: categorical(instead of one hot encoding, try response coding: use probability values), numerical features + project_title(BOW) + preprocessed_eassay (BOW) Set 2: categorical(instead of one hot encoding, try response coding: use probability values), numerical features + project_title(TFIDF)+ preprocessed_eassay (TFIDF) Set 3: categorical(instead of one hot encoding, try response coding: use probability values), numerical features + project_title(AVG W2V)+ preprocessed_eassay (AVG W2V). Here for this set take 20K datapoints only. Set 4: categorical(instead of one hot encoding, try response coding: use probability values), numerical features + project_title(TFIDF W2V)+ preprocessed_eassay (TFIDF W2V). Here for this set take 20K datapoints only. The hyper paramter tuning (Consider any two hyper parameters preferably n_estimators, max_depth) Consider the following range for hyperparameters n_estimators = [10, 50, 100, 150, 200, 300, 500, 1000], max_depth = [2, 3, 4, 5, 6, 7, 8, 9, 10] Find the best hyper parameter which will give the maximum AUC value Find the best hyper paramter using simple cross validation data You can write your own for loops to do this task Representation of results You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure with X-axis as n_estimators, Y-axis as max_depth, and Z-axis as AUC Score , we have given the notebook which explains how to plot this 3d plot, you can find it in the same drive 3d_scatter_plot.ipynb or You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure seaborn heat maps with rows as n_estimators, columns as max_depth, and values inside the cell representing AUC Score You can choose either of the plotting techniques: 3d plot or heat map Once after you found the best hyper parameter, you need to train your model with it, and find the AUC on test data and plot the ROC curve on both train and test. Along with plotting ROC curve, you need to print the confusion matrix with predicted and original labels of test data points Conclusion You need to summarize the results at the end of the notebook, summarize it in the table format. To print out a table please refer to this prettytable library link

dlschool

Официальный репозиторий курса Deep Learning (2018-2019) от Deep Learning School при ФПМИ МФТИ

donor_classification

Applying Multinomial Naive Bayes on the 'Donors Choose' and using Bag of Word(BOW) and Term Frequency-Inverse Document Frequency(TFIDF) Vectorisation for text features. To create a programming challenge, displaying the top features as given by feature_log_prob_ parameter of MultinomialNB is coded from scratch.

donors-choose-dt

Applying Decision Tree on Donors Choose Dataset

donors-choose-lr

Applying Logistic Regression on Donors Choose Dataset

donors-choose-rf

Applying Random Forest and GBDT on Donors Choose Dataset

donors-choose-truncated-svd

Apply Truncated SVD on Donors Choose dataset

donors-choose-tsne

Use TSNE to analyze Donors Choose Data Set

donors_choose_rf_and_gbdt

GBDT(Gradient Boosting Decision Tree) and RF(Random Forest) algorithm is applied on Donors Choose dataset.

downloads

eda

eda-1

Exploratory Data Analysis

eda-2

Exploratory Data Anaysis

facebook-friend-recommendation

facebookrecommendations

gbdt

human-activity-recognition_lstm

implement-sgd-classifier-with-logloss-and-l2-regularization-using-sgd-without-using-sklearn

Implement SGD Classifier with Logloss and L2 regularization Using SGD without using sklearn

implementing-logistic-regression-with-l2-regularisation-without-using-sklearn

Implementing Logistic Regression with L2 regularisation without using sklearn(manual implementation)

interview-question-data-science-