We have created a complete playlist of machine learning and deep learning projects videos with detailed explanation. In this video we have explained how to create a machine learning model with python. While building the machine learning project our developer takes care that you will learn from these videos a lot of things like how to perform exploratory data analysis, how to handle missing data, outlier, data visualisation, how to prepare data for building the machine learning model etc.
In this article, we are talking about the Bike Demand Prediction model. Here we will give you complete information about the Bike Demand Prediction model using python.
Bike sharing system is a method in which bikes are available for shared use to individuals for a short time period for a price or for free. Bike share systems allow people to borrow a bike from a dock which is usually computer controlled where the user gives the payment information and the system unlock it. After completing the journey, the bike can be returned to another dock belonging to the system.
Bike Demand is dependent on a few factors like how much data is available, date, hour, temperature, humidity, wind speed, visibility, dew points, solar radiation and rainfall etc. In bike demand business requirement weather plays a vital role. In this machine learning project we will choose the best approach to predict bike demand analysis.
To build the Bike demand prediction model we have used a seoul bike dataset which is also available on kaggle. The data file seoulbikedata.csv contains the information used to create the model. It consists of 8760 rows and 14da columns. The columns represent the variables, while the rows represent the instances.
The Dataset is composed of four concepts.
This dataset uses the following 14 variables:
Date : date on which day bike rent
Rented bike count : count Rent bike per day
Hour : How many hour rent on bike
Temperature : what temperature on the day when the bike on rent
Humidity : what Humidity on the day when the bike on rent
Wind speed : what Wind speed on the day when the bike on rent
Visibility : Visibility on the day when the bike on rent
Dew point temperature : what Dew point temperature on the day when the bike on rent
Solar Radiation : what Solar radiation on the day when the bike on rent
Rainfall : Rainfall on the day when the bike on rent
In our explanation video of Data-driven Bike Demand Prediction model using python, We cover techniques of exploratory analytics, data aggregation and cleansing, feature engineering, more importantly, model building and evaluation. We utilised Random Forest Classifier, Support Vector Machine and Logistic Regression with best parameters possible for getting the best prediction accuracy. All these algorithms are mathematical implementations and we have utilised them with optimal parameters.
The Bike Demand Prediction Project is described in two videos part 1 and part 2.
Part 1 : Title : BIKE DEMAND ANALYSIS Project Part 1 | AI ML Project Series
Description : This is the introduction part of the BIKE DEMAND ANALYSIS Project where we provide the details and procedures of the coming project that we will build in Part2 of this Series. This is based on analysis of hourly and daily bike demand in a city as Rented Bike Count where we have divided this count into categories and used them to analyse whether for some given conditions like weather, Holiday, Events and seasons, on a particular day or hour what will be the predicted demand of rented bikes. The result would make us more predictable towards what days showcase higher , lower or moderate demand for bikes.
Part 2 : Title : BIKE DEMAND ANALYSIS Project Part 2 | AI ML Project Series
Description : This is the second part of the BIKE DEMAND ANALYSIS Project where we create a complete project on Kaggle Community Platform regarding prediction of hourly or daily bike demand based on data over a year for a city. We use data cleaning, data plotting and utilised Random Forest Classifier, Support Vector Machine and Logistic Regression with best parameters possible for getting the best prediction accuracy. All these algorithms are mathematical implementations and we have utilised them with optimal parameters.
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