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a_b_testing

arl_association_rule_learning

autocomplete

Fig adds autocomplete to your terminal.

best-readme-template

An awesome README template to jumpstart your projects!

cagataytuylu

cagataytuylu.github.io

Jekyll Template - Mediumish

churn_prediction_with_pyspark

Churn Prediction with PySpark on local machine

customer-segmentation-with-rfm-and-k-means

demand_forecasting-with-time_series

house_price_prediction

House_Preci_Prediction

hybrid_recommender_system

model-selection-and-automated-hyperparameter-tuning

nlp_amazon_review_modeling

rating-product-sorting-reviews-in-amazon

rfm_cltv_prediction

smart_building_system-spark_streaming-kafka-

This dataset is collected from 255 sensor time series, instrumented in 51 rooms in 4 floors of the Sutardja Dai Hall(SDH) at UC Berkeley. It can be used to investigate patterns in physical properties of a room in a building. Moreover, it can also be used for experiments relating to Internet-of-Things (IoT), sensor fusion network or time-series tasks. This dataset is suitable for both supervised (classification and regression) and unsupervised learning (clustering) tasks. Each room includes 5 types of measurements: CO2 concentration, room air humidity, room temperature, luminosity, and PIR motion sensor data, collected over a period of one week from Friday, August 23, 2013 to Saturday, August 31, 2013. The PIR motion sensor is sampled once every 10 seconds and the remaining sensors are sampled once every 5 seconds. Each file contains the timestamps (in Unix Epoch Time) and actual readings from the sensor. The passive infrared sensor (PIR sensor) is an electronic sensor that measures infrared (IR) light radiating from objects in its field of view, which measures the occupancy in a room. Approximately 6% of the PIR data is non-zero, indicating an occupied status of the room. The remaining 94% of the PIR data is zero, indicating an empty room.