sainadh-k Goto Github PK

awesome-ipython-notebooks

complete-python-3-bootcamp

Course Files for Complete Python 3 Bootcamp Course on Udemy

introduction-to-time-series-forecasting-python

Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.

low-cost-carriers-versus-high-cost-carriers-using-airline-reviews

Is there really a difference? Low Cost Carriers versus High Cost Carriers using airline reviews

machine-learning-101

Hello friends, I am making a Machine Learning repo. where I will upload several datasets and its solution with explanation. Starting from the basic and moving up in difficulty level.

machine-learning-a-z

Machine learning A-Z course from Udemy

ml-twitter-sentiment-analysis

Jupyter Notebook + Python code of twitter sentiment analysis

nlp-in-practice

Starter code to solve real world text data problems. Includes: Gensim Word2Vec, phrase embeddings, keyword extraction with TFIDF, Text Classification with Logistic Regression, word count with pyspark, simple text preprocessing, pre-trained embeddings and more.

online_retail_customer-segementation

Introduction In ecommerce companies like online retails, customer segmentation is necessary in order to understand customers behaviors. It leverages aqcuired customer data like the one we have in our case, transactions data in order to divide customers into groups. Our goal in this Notebook is to cluster our customers to get insights in: Increasing revenue (Knowing customers who present most of our revenue) Increasing customer retention Discovering Trends and patterns Defining customers at risk We will do RFM Analysis as a first step and then combine RFM with predictive algorithms (k-means). RFM Analysis answers these questions: Who are our best customers? Who has the potential to be converted in more profitable customers? Which customers we must retain? Which group of customers is most likely to respond to our current campaign? More about RFM here.

python-data-science-and-machine-learning-bootcamp

text_historical_authenticity_evaluation

A study on the historical authenticity of a text. The historical authenticity is evaluated by comparing the frequencies of unigrams, bigrams and trigrams of a given text to the frequencies of the ngrams of texts written in the period of +/- 5 years from the claimed date of the release of the given text and to the frequency of the ngrams of recent texts. A tool to visualise the findings made with pandas and matplotlib-pyplot is included.

time-series-analysis-using-python

Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data.

udacity-predicting-boston-housing-prices

upgrad_ml_ai

UpGrad Course workout for ML & AI

walmart-sales-prediction