├── 1. data_cleaning_project ├── README.md ├── clean_csi_timeseries.csv ├── csi_data_cleaning.html ├── csi_data_cleaning.ipynb └── data │ ├── Advance Retail Sales_RSXFS.csv │ ├── Civilian Unemployment Rate UNRATE.csv │ ├── Crude Oil Prices MCOILBRENTEU.csv │ ├── Dow Jones Industrial Average DJI.csv │ ├── Federal Interest Rates FEDFUNDS.csv │ ├── GDP.csv │ ├── Housing Starts HOUST.csv │ ├── Total Vehicle SalesTOTALSA .csv │ └── consumer_sent_UMCH_tbmics.xls ├── 2. data_storytelling_project ├── README.md ├── Thumbs.db ├── chicago_commuity _areas_map.png ├── chicago_police_districts.geojson ├── df_homicide.pkl ├── homicide.data.cityofchicago.org.html ├── homicide.data.cityofchicago.org.ipynb ├── map1.html ├── map2.html ├── map3.html ├── map4.html ├── map5.html └── map6.html ├── 3. NLP_twitter_sentiment_analysis ├── FINAL____twitter_sentiment_twitter.html ├── FINAL____twitter_sentiment_twitter.ipynb ├── README.md ├── Thumbs.db ├── df_paulry.p ├── df_paulry_longs_lats.p ├── img_map.html ├── naive_bayes_rule.png ├── paul_ryan_twitter.csv ├── paulry_predicts_df.p ├── requirements.txt ├── twitter_sentiment.pkl └── twitter_us_map.html ├── NB Spam filter ├── Bayesian_Inference.html ├── Bayesian_Inference.ipynb ├── SMSSpamCollection ├── images │ ├── bayes_formula.png │ ├── countvectorizer.png │ ├── dqnb.png │ ├── naivebayes.png │ └── tfidf.png └── readme └── README.md /1. data_cleaning_project/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RonKG/Machine-Learning-Projects-2/HEAD/1. data_cleaning_project/README.md -------------------------------------------------------------------------------- /1. data_cleaning_project/clean_csi_timeseries.csv: 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