Drew-9960

Ad_Campaign

Churn-Modeling-testing-Aritifical-Neural-Nets-Logistic-Regression-KNN-LightGBM-Classifier-

Classification-Bagging-Boosting

Clustering_Analysis_of_Toronto_Venues

Insurance_Premium_Prediction

Interactive_VisBokeh

Job-in-Degree-Analysis

linearRegression

Pitch_Prediction

Baseball Pitch Prediction

Sentiment-Analysis_Mental-Health

Drew-9960/Ad_Campaign

Drew-9960/Churn-Modeling-testing-Aritifical-Neural-Nets-Logistic-Regression-KNN-LightGBM-Classifier-

Drew-9960/Classification-Bagging-Boosting

Drew-9960/Clustering_Analysis_of_Toronto_Venues

Drew-9960/Insurance_Premium_Prediction

Drew-9960/Interactive_VisBokeh

Drew-9960/Job-in-Degree-Analysis

Drew-9960/linearRegression

Drew-9960/Pitch_Prediction

Baseball Pitch Prediction

Drew-9960/Sentiment-Analysis_Mental-Health

Drew-9960/LogisticRegression

Drew-9960/RandomForest

Drew-9960/Survey-Exploration

A survey was conducted to gauge an audience interest in different data science topics, namely: Big Data (Spark / Hadoop) Data Analysis / Statistics Data Journalism Data Visualization Deep Learning Machine Learning The participants had three options for each topic: Very Interested, Somewhat interested, and Not interested. 2,233 respondents completed the survey. Manipulating and cleaning the data I then followed up by using the artist layer of Matplotlib to create a bar chart to visualize the percentage of the respondents interest in the different data science topics surveyed.

Drew-9960/TimeSeries