Bike_Sharing_Project

This project was developed for SIH 2020 Internal. Problem statement was provided by ISRO. All the datasets used are open source and freely available on the internet. Primary technology used to develop this project are Python, Machine Learning and Jupyter Notebook

This repository contains the Python codes for simple problem statements. These code are beneficial for those who wants to learn Python by actually coding. For better comprehension, the details of each line of code is also shared in the jupyter notebooks

# Forecasting Stock Market Prices It is a **Time Series** dataset.A time series is simply a series of data points ordered in time.In a time series, time is often the independent variable and the goal is usually to make a forecast for the future. ## PROBLEM STATEMENT: Our Aim is to create a model that can forecast the future stock price based on the model training and provided dataset. ### Data We will be using a [Huge stock market dataset](https://www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs) from the Kaggle platform which has a very good collection of datasets.The file we will be using is present in following directory in the dataset zip file input\Data\Stocks\gs.us.txt The data is presented in CSV format as follows : Date, Open, High, Low, Close, Volume, OpenInt. Features: - Date - Open - High - Low - Close - Volume - OpenInt Note that prices have been adjusted for dividends and splits. ### LICENSE OF DATASET : [LICENSE](https://creativecommons.org/publicdomain/zero/1.0/) ### Requirements You will also need to have software installed to run and execute a [Jupyter Notebook](http://ipython.org/notebook.html) If you do not have Python installed yet, it is highly recommended that you install the [Anaconda](http://continuum.io/downloads) distribution of Python, which already has the above packages and more included. This project requires **Python** and the following Python libraries installed: - [NumPy](http://www.numpy.org/) - [Pandas](http://pandas.pydata.org/) - [matplotlib](http://matplotlib.org/) - [scikit-learn](http://scikit-learn.org/stable/) - [statsmodels](https://www.statsmodels.org/stable/) ### Run In a terminal or command window, navigate to the top-level project directory `STOCK MARKET FORECASTING/` (that contains this README) and run one of the following commands: ipython notebook Forecasting_Stock_Market_Prices_task.ipynb or jupyter notebook Forecasting_Stock_Market_Prices_task.ipynb This will open the Jupyter Notebook software and project file in your browser. ### Steps : 1. Importing Libraries 2. Exploring the Dataset 3. Exploratory Data Analysis > * Univariate Analysis 4. Data Preprocessing 5. Model Building > * AUTOREGRESSIVE MODEL > * MOVING AVERAGE MODEL 6. Evaluation > * MEAN SQUARE ERROR > * MEAN ABSOLUTE ERROR > * ROOT MEAN SQUARE ERROR 7. Conclusion

This assignment is a programming assignment wherein you have to build a multiple linear regression model for the prediction of demand for shared bikes. You will need to submit a Jupyter notebook for the same. Problem Statement A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" which is usually computer-controlled wherein the user enters the payment information, and the system unlocks it. This bike can then be returned to another dock belonging to the same system. A US bike-sharing provider BoomBikes has recently suffered considerable dips in their revenues due to the ongoing Corona pandemic. The company is finding it very difficult to sustain in the current market scenario. So, it has decided to come up with a mindful business plan to be able to accelerate its revenue as soon as the ongoing lockdown comes to an end, and the economy restores to a healthy state. In such an attempt, BoomBikes aspires to understand the demand for shared bikes among the people after this ongoing quarantine situation ends across the nation due to Covid-19. They have planned this to prepare themselves to cater to the people's needs once the situation gets better all around and stand out from other service providers and make huge profits. They have contracted a consulting company to understand the factors on which the demand for these shared bikes depends. Specifically, they want to understand the factors affecting the demand for these shared bikes in the American market. The company wants to know: Which variables are significant in predicting the demand for shared bikes. How well those variables describe the bike demands Based on various meteorological surveys and people's styles, the service provider firm has gathered a large dataset on daily bike demands across the American market based on some factors. Business Goal: You are required to model the demand for shared bikes with the available independent variables. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations. Further, the model will be a good way for management to understand the demand dynamics of a new market. Data Preparation: You can observe in the dataset that some of the variables like 'weathersit' and 'season' have values as 1, 2, 3, 4 which have specific labels associated with them (as can be seen in the data dictionary). These numeric values associated with the labels may indicate that there is some order to them - which is actually not the case (Check the data dictionary and think why). So, it is advisable to convert such feature values into categorical string values before proceeding with model building. Please refer the data dictionary to get a better understanding of all the independent variables. You might notice the column 'yr' with two values 0 and 1 indicating the years 2018 and 2019 respectively. At the first instinct, you might think it is a good idea to drop this column as it only has two values so it might not be a value-add to the model. But in reality, since these bike-sharing systems are slowly gaining popularity, the demand for these bikes is increasing every year proving that the column 'yr' might be a good variable for prediction. So think twice before dropping it. Model Building In the dataset provided, you will notice that there are three columns named 'casual', 'registered', and 'cnt'. The variable 'casual' indicates the number casual users who have made a rental. The variable 'registered' on the other hand shows the total number of registered users who have made a booking on a given day. Finally, the 'cnt' variable indicates the total number of bike rentals, including both casual and registered. The model should be built taking this 'cnt' as the target variable. Model Evaluation: When you're done with model building and residual analysis and have made predictions on the test set, just make sure you use the following two lines of code to calculate the R-squared score on the test set. from sklearn.metrics import r2_score r2_score(y_test, y_pred) where y_test is the test data set for the target variable, and y_pred is the variable containing the predicted values of the target variable on the test set. Please don't forget to perform this step as the R-squared score on the test set holds some marks. The variable names inside the 'r2_score' function can be different based on the variable names you have chosen.

HELP International is an international humanitarian NGO that is committed to fighting poverty and providing the people of backward countries with basic amenities and relief during the time of disasters and natural calamities. It runs a lot of operational projects from time to time along with advocacy drives to raise awareness as well as for funding purposes. After the recent funding programmes, they have been able to raise around $ 10 million. Now the CEO of the NGO needs to decide how to use this money strategically and effectively. The significant issues that come while making this decision are mostly related to choosing the countries that are in the direst need of aid. And this is where you come in as a data analyst. Your job is to categorise the countries using some socio-economic and health factors that determine the overall development of the country. Then you need to suggest the countries which the CEO needs to focus on the most. The datasets containing those socio-economic factors and the corresponding data dictionary are provided below. Objectives Your main task is to cluster the countries by the factors mentioned above and then present your solution and recommendations to the CEO using a PPT. The following approach is suggested : Start off with the necessary data inspection and EDA tasks suitable for this dataset - data cleaning, univariate analysis, bivariate analysis etc. Outlier Analysis: You must perform the Outlier Analysis on the dataset. However, you do have the flexibility of not removing the outliers if it suits the business needs or a lot of countries are getting removed. Hence, all you need to do is find the outliers in the dataset, and then choose whether to keep them or remove them depending on the results you get. Try both K-means and Hierarchical clustering(both single and complete linkage) on this dataset to create the clusters. [Note that both the methods may not produce identical results and you might have to choose one of them for the final list of countries.] Analyse the clusters and identify the ones which are in dire need of aid. You can analyse the clusters by comparing how these three variables - [gdpp, child_mort and income] vary for each cluster of countries to recognise and differentiate the clusters of developed countries from the clusters of under-developed countries. Also, you need to perform visualisations on the clusters that have been formed. You can do this by choosing any two of the three variables mentioned above on the X-Y axes and plotting a scatter plot of all the countries and differentiating the clusters. Make sure you create visualisations for all the three pairs. You can also choose other types of plots like boxplots, etc. Both K-means and Hierarchical may give different results because of previous analysis (whether you chose to keep or remove the outliers, how many clusters you chose, etc.) Hence, there might be some subjectivity in the final number of countries that you think should be reported back to the CEO since they depend upon the preceding analysis as well. Here, make sure that you report back at least 5 countries which are in direst need of aid from the analysis work that you perform. Results Expected A well-commented Jupyter notebook containing the Clustering Models(both K-means and Hierarchical Clustering) and the final list of countries. Present the overall approach of the analysis in a presentation Mention the problem statement and the analysis approach. Explain the results of Clustering Model briefly. Include visualisations and summarise the most important results in the presentation. Make sure that you mention the final list of countries here ( Don't just mention the cluster id or cluster name here. Mention the names of all the countries.)