Final-Assignment-IBM-TDS

Part I - WeatherPy In this example, you’ll be creating a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, you’ll be utilizing a simple Python library, the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities. Your first objective is to build a series of scatter plots to showcase the following relationships: Temperature (F) vs. Latitude Humidity (%) vs. Latitude Cloudiness (%) vs. Latitude Wind Speed (mph) vs. Latitude After each plot add a sentence or too explaining what the code is and analyzing. Your next objective is to run linear regression on each relationship, only this time separating them into Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude): Northern Hemisphere - Temperature (F) vs. Latitude Southern Hemisphere - Temperature (F) vs. Latitude Northern Hemisphere - Humidity (%) vs. Latitude Southern Hemisphere - Humidity (%) vs. Latitude Northern Hemisphere - Cloudiness (%) vs. Latitude Southern Hemisphere - Cloudiness (%) vs. Latitude Northern Hemisphere - Wind Speed (mph) vs. Latitude Southern Hemisphere - Wind Speed (mph) vs. Latitude After each pair of plots explain what the linear regression is modelling such as any relationships you notice and any other analysis you may have. Your final notebook must: Randomly select at least 500 unique (non-repeat) cities based on latitude and longitude. Perform a weather check on each of the cities using a series of successive API calls. Include a print log of each city as it’s being processed with the city number and city name. Save a CSV of all retrieved data and a PNG image for each scatter plot. Part II - VacationPy Now let’s use your skills in working with weather data to plan future vacations. Use jupyter-gmaps and the Google Places API for this part of the assignment. Note: if you having trouble displaying the maps try running jupyter nbextension enable --py gmaps in your environment and retry. Create a heat map that displays the humidity for every city from the part I of the homework. heatmap Narrow down the DataFrame to find your ideal weather condition. For example: A max temperature lower than 80 degrees but higher than 70. Wind speed less than 10 mph. Zero cloudiness. Drop any rows that don’t contain all three conditions. You want to be sure the weather is ideal. Note: Feel free to adjust to your specifications but be sure to limit the number of rows returned by your API requests to a reasonable number. Using Google Places API to find the first hotel for each city located within 5000 meters of your coordinates. Plot the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country. hotel map As final considerations: Create a new GitHub repository for this project called API-Challenge (note the kebab-case). Do not add to an existing repo You must complete your analysis using a Jupyter notebook. You must use the Matplotlib or Pandas plotting libraries. For Part I, you must include a written description of three observable trends based on the data. You must use proper labeling of your plots, including aspects like: Plot Titles (with date of analysis) and Axes Labels. For max intensity in the heat map, try setting it to the highest humidity found in the data set. Hints and Considerations The city data you generate is based on random coordinates as well as different query times; as such, your outputs will not be an exact match to the provided starter notebook. You may want to start this assignment by refreshing yourself on the geographic coordinate system. Next, spend the requisite time necessary to study the OpenWeatherMap API. Based on your initial study, you should be able to answer basic questions about the API: Where do you request the API key? Which Weather API in particular will you need? What URL endpoints does it expect? What JSON structure does it respond with? Before you write a line of code, you should be aiming to have a crystal clear understanding of your intended outcome. A starter code for Citipy has been provided. However, if you’re craving an extra challenge, push yourself to learn how it works: citipy Python library. Before you try to incorporate the library into your analysis, start by creating simple test cases outside your main script to confirm that you are using it correctly. Too often, when introduced to a new library, students get bogged down by the most minor of errors – spending hours investigating their entire code – when, in fact, a simple and focused test would have shown their basic utilization of the library was wrong from the start. Don’t let this be you! Part of our expectation in this challenge is that you will use critical thinking skills to understand how and why we’re recommending the tools we are. What is Citipy for? Why would you use it in conjunction with the OpenWeatherMap API? How would you do so? In building your script, pay attention to the cities you are using in your query pool. Are you getting coverage of the full gamut of latitudes and longitudes? Or are you simply choosing 500 cities concentrated in one region of the world? Even if you were a geographic genius, simply rattling 500 cities based on your human selection would create a biased dataset. Be thinking of how you should counter this. (Hint: Consider the full range of latitudes). Once you have computed the linear regression for one chart, the process will be similar for all others. As a bonus, try to create a function that will create these charts based on different parameters. Remember that each coordinate will trigger a separate call to the Google API. If you’re creating your own criteria to plan your vacation, try to reduce the results in your DataFrame to 10 or fewer cities. Lastly, remember – this is a challenging activity. Push yourself! If you complete this task, then you can safely say that you’ve gained a strong mastery of the core foundations of data analytics and it will only go better from here. Good luck!

Introduction : This data set is a masked data set which is similar to what data analysts at Uber handle. Solving this assignment will give you an idea about how problems are systematically solved using EDA and data visualisation. Business Understanding : You may have some experience of travelling to and from the airport. Have you ever used Uber or any other cab service for this travel? Did you at any time face the problem of cancellation by the driver or non-availability of cars? Well, if these are the problems faced by customers, these very issues also impact the business of Uber. If drivers cancel the request of riders or if cars are unavailable, Uber loses out on its revenue. As an analyst, you decide to address the problem Uber is facing - driver cancellation and non-availability of cars leading to loss of potential revenue. Business Objectives : The aim of analysis is to identify the root cause of the problem (i.e. cancellation and non-availability of cars) and recommend ways to improve the situation. As a result of your analysis, you should be able to present to the client the root cause(s) and possible hypotheses of the problem(s) and recommend ways to improve them. There are six attributes associated with each request made by a customer: 1. Request id: A unique identifier of the request 2. Time of request: The date and time at which the customer made the trip request 3. Drop-off time: The drop-off date and time, in case the trip was completed 4. Pick-up point: The point from which the request was made 5. Driver id: The unique identification number of the driver 6. Status of the request: The final status of the trip, that can be either completed, cancelled by the driver or no cars available Note: For this assignment, only the trips to and from the airport are being considered. Results Expected : 1. Visually identify the most pressing problems for Uber. Hint: Create plots to visualise the frequency of requests that get cancelled or show 'no cars available'; identify the most problematic types of requests (city to airport / airport to city etc.) and the time slots (early mornings, late evenings etc.) using plots. 2. Find out the gap between supply and demand and show the same using plots. a. Find the time slots when the highest gap exists b. Find the types of requests (city-airport or airport-city) for which the gap is the most severe in the identified time slots 3. What do you think is the reason for this issue for the supply-demand gap? Write the answer in less than 100 words. You may accompany the write-up with plot(s). 4. Recommend some ways to resolve the supply-demand gap. IDE : jupyter notebook Language : Python Libraries : Numpy, Pandas, Matplotlib, Seaborn Please do explore the dataset further to your own and see what kind of other insights you can get across various other columns.

This project investigated the the Drossel-Schwabl forest fire model, as part of the module "Scientific Computing Laboratory 2". This repository contains the final report, in the form of a Jupyter notebook and a pdf file detailing the aims of the project. I greatly enjoyed the exploratory aspect of this assignment and achieved 54 out of 60 points, or 90%

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 : Perform PCA on the dataset and obtain the new dataset with the Principal Components. Choose the appropriate number of components k. You need to perform your clustering activity on this new dataset, i.e. the PCA modified dataset with the k components. Outlier Analysis: You must perform the Outlier Analysis on the dataset, before or after performing PCA, as per your choice. 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. Note that you perform clustering on the PCA modified dataset and the clusters that are formed are being analysed now using the original variables to identify the countries which you finally want to select. Also, you need to perform visualisations on the clusters that have been formed. You can do this by choosing the first two Principal Components (on the X-Y axes) and plotting a scatter plot of all the countries and differentiating the clusters. You should also do the same visualisation using any two of the original variables (like gdpp, child_mort, etc.) on the X-Y axes as well. You can also choose other types of plots like boxplots, etc. The final list of countries depends on the number of components that you choose and the number of clusters that you finally form. Also, both K-means and Hierarchical may give different results. Hence, there might be some subjectivity in the final number of countries that you think should be reported back to the CEO. 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. The clusters must be visualised on both the Principal Components and some of the original variables. Present the overall approach of the analysis in a presentation Mention the problem statement and the analysis approach. Explain the results of Principal Component Analysis and Clustering 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.)

## WeatherPy In this example, you'll be creating a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, you'll be utilizing a [simple Python library](https://pypi.python.org/pypi/citipy), the [OpenWeatherMap API](https://openweathermap.org/api), and a little common sense to create a representative model of weather across world cities. Your objective is to build a series of scatter plots to showcase the following relationships: * Temperature (F) vs. Latitude * Humidity (%) vs. Latitude * Cloudiness (%) vs. Latitude * Wind Speed (mph) vs. Latitude Your final notebook must: * Randomly select **at least** 500 unique (non-repeat) cities based on latitude and longitude. * Perform a weather check on each of the cities using a series of successive API calls. * Include a print log of each city as it's being processed with the city number and city name. * Save both a CSV of all data retrieved and png images for each scatter plot. As final considerations: * You must complete your analysis using a Jupyter notebook. * You must use the Matplotlib or Pandas plotting libraries. * You must include a written description of three observable trends based on the data. * You must use proper labeling of your plots, including aspects like: Plot Titles (with date of analysis) and Axes Labels. * See [Example Solution](WeatherPy_Example.pdf) for a reference on expected format. ## Hints and Considerations * You may want to start this assignment by refreshing yourself on the [geographic coordinate system](http://desktop.arcgis.com/en/arcmap/10.3/guide-books/map-projections/about-geographic-coordinate-systems.htm). * Next, spend the requisite time necessary to study the OpenWeatherMap API. Based on your initial study, you should be able to answer basic questions about the API: Where do you request the API key? Which Weather API in particular will you need? What URL endpoints does it expect? What JSON structure does it respond with? Before you write a line of code, you should be aiming to have a crystal clear understanding of your intended outcome. * A starter code for Citipy has been provided. However, if you're craving an extra challenge, push yourself to learn how it works: [citipy Python library](https://pypi.python.org/pypi/citipy). Before you try to incorporate the library into your analysis, start by creating simple test cases outside your main script to confirm that you are using it correctly. Too often, when introduced to a new library, students get bogged down by the most minor of errors -- spending hours investigating their entire code -- when, in fact, a simple and focused test would have shown their basic utilization of the library was wrong from the start. Don't let this be you! * Part of our expectation in this challenge is that you will use critical thinking skills to understand how and why we're recommending the tools we are. What is Citipy for? Why would you use it in conjunction with the OpenWeatherMap API? How would you do so? * In building your script, pay att