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-reversing--python-fun-

reverse in python

back-propagation-algorithm

//Back-propagation Algorithm

chess-game-rook-and-pawn

Write a python program to do the following: Given the location of a rook(x1, y1) on a chess board and location of a pawn (x2,y2). Find the minimum number of moves it will take for the rook to kill the pawn. Please bear in mind that 1. it is an 8x8 board 2. There are no other pieces on the board.

data_science_blogs

A repository to keep track of all the code that I end up writing for my blog posts.

dataframe_pandas

how to create dataframe using pandas

datascienceexamples

Examples on data science as reference to blog posts.

decision-trees-on-amazon-reviews-data-set

decision Tree algorithm is applied on amazon reviews datasets to predict whether a review is positive or negative. Procedure to execute the above task is as follows: • Step1: Data Pre-processing is applied on given amazon reviews data-set.And Take sample of data from dataset because of computational limitations • Step2: Time based splitting on train and test datasets. • Step3: Apply Feature generation techniques(avg w2v,tfidfw2v) • Step4: Apply Decision Tree algorithm using each technique. • Step5: To find C(1/lambda) and gamma(=1/sigma). • Step6: Decision tree Feature Importance using BOW and TF-IDF • Step7: Images of Decision tree in png format with verious vectorizations. 0.2 Objective: • To classify given reviews (positive (Rating of 4 or 5) & negative (rating of 1 or 2)) using Decision Trees algorithm.

design-2

leetcode data structure problems

eda-operations-on-haberman-s-survival-data

exercise,EDA(Exploratory Data Analysis) is performed on Haberman’s Survival DataSet to analyze the dataset’s main characteristics in visual way.The dataset is about survival of patients who had undergone surgery for breast cancer. ### Objective: * To predict and analyze the data regarding the survival of patients who had undergone breast cancer based on the patient’s age, year of treatment and the number of positive lymph nodes. * To visualize the data

fb-profanity-check

Imagine there is a file full of Facebook comments by various users and you are provided a set of words that signify profanity. Can you write a program which can indicate the degree of profanity for each sentence in the file?

first_program-sub-python3.6

Take two numbers and compute the subtraction in such way that output is always positive numbers

flask-upload

Web app to upload and read the file

gbdt-and-rf-to-amazon-reviews-dataset

GBDT(Gradient Boosting Decision Tree) and RF(Random Forest) algorithm is applied on amazon reviews datasets to predict whether a review is positive or negative. Procedure to execute the above task is as follows: • Step1: Data Pre-processing is applied on given amazon reviews data-set. • Step2: Time based splitting on train and test datasets. • Step3: Apply Feature generation techniques(BOW,TF-IDF,avg w2v,tfidfw2v) • Step4: Apply GBDT(Gradient Boosting Decision Tree) algorithm using each technique. • Step5: Apply RF(Random Forest) algorithm using each technique. • Step6: To find Number of Base learners(m) using gridsearch cross-validation in case of RF(Random Forest) algorithm . • Step7: To find Number of Base learners(m),depth,learning rate(v) using gridsearch crossvalidation in case of RF(Random Forest) algorithm. 0.2 Objective: • To classify given reviews (positive (Rating of 4 or 5) & negative (rating of 1 or 2)) using GBDT(Gradient Boosting Decision Tree) and RF(Random Forest) algorithm .

human-activity-detection-m-

Objective: Various Models with different no. of hidden layer and optimizer with different dropout in LSTM

human-activity-recognition

This project is to build a model that predicts the human activities such as Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing or Laying. This dataset is collected from 30 persons(referred as subjects in this dataset), performing different activities with a smartphone to their waists. The data is recorded with the help of sensors (accelerometer and Gyroscope) in that smartphone. This experiment was video recorded to label the data manually.

implement-sgd-for-linear-regression

0.1 Assignment 6: Implement SGD for linear regression To implement stochastic gradient descent to optimize a linear regression algorithm on Boston House Prices dataset which is already exists in sklearn as a sklearn.linear_model.SGDRegressor.here,SGD algorithm is defined manually and then comapring the both results.Linear regression is technique to predict on real values. ##### stochastic gradient descent technique , evaluates and updates the coefficients every iteration to minimize the error of a model on training data. 0.2 Objective: To Implement stochastic gradient descent on Bostan House Prices dataset for linear Regression • Implement SGD and deploy on Bostan House Prices dataset. • Comapare the Results with sklearn.linear_model.SGDRegressor

k-means-clustering-hierarchical-clustering-and-dbscan-to-amazon-food-reviews-dataset

Clustering algorithm is applied on amazon reviews datasets to cluster the reviews. Types of clustering : 1.K-Means clustering 2.Hierarchical Clustering 3.DBSCAN(Density Based spital clustering of application with noise).

k-nn-on-amazon-reviews-data-set

K-NN is used for classification and regression for data.Here, K-NN algorithm is applied on amazon reviews datasets to classify postive and negative reviews.

kfold-cross-validation

for finding accuracy of data ,how we can use kfold cross validation

kmedoids

The Python implementation of k-medoids.

leap2.0

Leap 2.0 is a platform offered by Complete open Source Solutions(COSS),where students from different campus irrespective of all platforms can join to explore themselves with the limitless IT world by gaining knowledge on open source cutting edge technologies on demand and get opportunity to work on live projects.

leetcode_problem

LEETCODE PROBLEM Solutions

logistic-regression-on-amazon-reviews-data-set.

Logistic Regression algorithm is applied on amazon reviews datasets to predict whether a review is positive or negative. Procedure to execute the above task is as follows: • Step1: Data Pre-processing is applied on given amazon reviews data-set.And Take sample of data from dataset because of computational limitations • Step2: Time based splitting on train and test datasets. • Step3: Apply Feature generation techniques(Bow,tfidf,avg w2v,tfidfw2v) • Step4: Apply Logistic Regression algorithm using each technique. • Step5: To find lambda using gridsearch cross-validation and random cross-validation • Step5: L1 and L2 regularization • Step6: L1 Regularization- Increase lambda hyperparameter to generate sparcity in dataset. 1. Report Performance metric 2. Report Error 3. Report Sparcity in "W*" • Step6: Feature Importance for postive and Negative reviews 1. Most Important Feature 2. Bar plot of top 15 Important Features. 0.2 Objective: • To classify given reviews (positive (Rating of 4 or 5) & negative (rating of 1 or 2)) using Logistic regression algorithm.

lstm-on-amazon-reviews-dataset

naive-bayes-on-amazon-reviews-data-set

Naive Bayes works on Bayes theorem of probability to predict the class of unknown data set.Here, Naive Bayes algorithm is applied on amazon reviews datasets to classify postive and negative reviews.

react-flask

reading-csv-file

CSV file reading and processing on it

revenue-forecasting-for-finance-dataset

Revenue forecasting is project of finance datasets where Forecasting is done from empirical model.

reviews

stack-overflow-tag-prediction