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sdjangam

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Global Rank 571,305 (Top 20 %)
Followers 24
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Registered over 4 years ago
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83.3 %

R
11.1 %

C++
5.6 %
Location 🇮🇳 India
Country Total Rank 23,307
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R
431

Jupyter Notebook
2,192

Python

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Deep-learning-

Deep learning for data science

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Learning-various-classifiers-on-Iris-dataset

Performing the model like Decision Tree Page. Coding up KNN from scratch in Python. KNN using SKlearn. Navie Bayes using SKlearn. Boosting in Python. Feature importance using ensemble Classifiers. Voting Classifiers using SKLearn.

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Twitter-Sentiment-Analysis

Investigation of open data from internet-based expressions and opinions could yield fascinating outcomes and bits of knowledge into the universe of popular feelings about any item, administration or identity. The blast of Web 2.0 has prompted expanded action in Podcasting, Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. Subsequently there has been a sudden increase of enthusiasm for individuals to mine these tremendous assets of information for suppositions. Sentiment analysis or Opinion Mining is mining of sentiment polarities from online social media. In this project we will talk about a procedure which permits use and understanding of twitter information for sentiment analysis. We perform several steps of text pre-processing, and then experiment with multiple classification mechanisms. Using a dataset of 50000 tweets and TFIDF features, we comparison the accuracy obtained using various classifiers for this task. We find that linear SVMs provide us the best accuracy results among the various classifiers tried. Sentiment analysis classifier could be useful for many applications like market analysis of different features of a new product or public opinion for a new movie or speech by a political candidate.

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Logistic-regression-on-the-titanic-dataset

Performing the model like Logistic regression on the titanic dataset. Visualizing a logistic regression model.

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R and related to all basic concepts

Data-visualisation-

Data visualisation for data science

R-Mini-Project---Analysis-using-R-Why-Are-Low-Quality-Diamonds-More-Expensive-

learning variance and covariance

Data-mining

Data mining for data science

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Python-Mini-Project---Data-Analysis-and-Prediction-using-the-Loan-Prediction-Dataset

We have the loan application information like the applicant's name, personal details, financial information and requested loan amount and related details and the outcome (whether the application was approved or rejected). Based on this we are going to train a model and predict if a loan will get approved or not.

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Text-mining-and-Analytics-

Text mining and analytics for data science

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Machine-learning-

Data science for machine learning

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Web-mining

Web mining for data science

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Data-collection-

Data collection for data science

C++

Python-Mini-Project---Word-Ladders-Game

You are given two words of the same length: e.g., cold and warm ! Your problem is to find a chain of words from the starting word cold to the ending word warm so that each successive word differs from the previous in exactly one letter. ! Example: cold, cord, card, ward, warm.

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Probability-and-Statistics-

Data science for probability and statistics

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Hand-Written-Digit-Recognition

Performing the model like MLP for Hand-written digit recognition with no hidden layer with 10 output neurons. MLP for Hand-written digit recognition with two hidden layers.

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Use-CoNLL-2002-data-to-build-a-NER-system

Making module like CoNLL 2002 data to build a NER system Understand the dataset. Use CoNLL 2002 data to build a NER system Define features. Use CoNLL 2002 data to build a NER system Learn and evaluate the CRF. Use CoNLL 2002 data to build a NER system- Hyper-parameter Optimization. Use CoNLL 2002 data to build a NER system Feature Importances.

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Churn-Prediction

Customers of a big international bank decided to leave the bank. The bank is investigating a very high rate of customer leaving the bank. The dataset contains 10000 records, and we use it to investigate and predict which of the customers are more likely to leave the bank soon. The approach here is supervised classification; the classification model to be built on historical data and then used to predict the classes for the current customers to identify the churn. The dataset contains 13 features, and also the label column (Exited or not). The best accuracy was obtained with the Naïve Bayes model (83.29%). Such churn prediction models could be very useful for applications such as churn prediction in Telecom sector to identify the customers who are switching from current network, and also for Churn prediction in subscription services.

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sdjangam

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