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!

A structured collection of Pandas practice notes and data manipulation workflows, converted from Jupyter Notebooks into standalone modular Python scripts. Covers data exploration, filtering, aggregation, pivot tables, string cleaning, datetime handling, and data pipeline tasks.

Data Analysis School Module 2: Comprehensive Python for Data Analysis course notes covering Python basics, NumPy, Pandas, Matplotlib and Seaborn. 5 structured Jupyter Notebook lessons for learning data analysis.

## Instructions ### Step 1: Preprocess the data Using your knowledge of Pandas and the Scikit-Learn’s `StandardScaler()`, you’ll need to preprocess the dataset in order to compile, train, and evaluate the neural network model later in Step 2 Using the information we have provided in the starter code, follow the instructions to complete the preprocessing steps. 1. Read in the charity_data.csv to a Pandas DataFrame, and be sure to identify the following in your dataset: * What variable(s) are considered the target(s) for your model? * What variable(s) are considered the feature(s) for your model? 2. Drop the `EIN` and `NAME` columns. 3. Determine the number of unique values for each column. 4. For those columns that have more than 10 unique values, determine the number of data points for each unique value. 6. Use the number of data points for each unique value to pick a cutoff point to bin "rare" categorical variables together in a new value, `Other`, and then check if the binning was successful. 7. Use `pd.get_dummies()` to encode categorical variables ### Step 2: Compile, Train, and Evaluate the Model Using your knowledge of TensorFlow, you’ll design a neural network, or deep learning model, to create a binary classification model that can predict if an Alphabet Soup–funded organization will be successful based on the features in the dataset. You’ll need to think about how many inputs there are before determining the number of neurons and layers in your model. Once you’ve completed that step, you’ll compile, train, and evaluate your binary classification model to calculate the model’s loss and accuracy. 1. Continue using the jupter notebook where you’ve already performed the preprocessing steps from Step 1. 2. Create a neural network model by assigning the number of input features and nodes for each layer using Tensorflow Keras. 3. Create the first hidden layer and choose an appropriate activation function. 4. If necessary, add a second hidden layer with an appropriate activation function. 5. Create an output layer with an appropriate activation function. 6. Check the structure of the model. 7. Compile and train the model. 8. Create a callback that saves the model's weights every 5 epochs. 9. Evaluate the model using the test data to determine the loss and accuracy. 10. Save and export your results to an HDF5 file, and name it `AlphabetSoupCharity.h5`. ### Step 3: Optimize the Model Using your knowledge of TensorFlow, optimize your model in order to achieve a target predictive accuracy higher than 75%. If you can't achieve an accuracy higher than 75%, you'll need to make at least three attempts to do so. Optimize your model in order to achieve a target predictive accuracy higher than 75% by using any or all of the following: * Adjusting the input data to ensure that there are no variables or outliers that are causing confusion in the model, such as: * Dropping more or fewer columns. * Creating more bins for rare occurrences in columns. * Increasing or decreasing the number of values for each bin. * Adding more neurons to a hidden layer. * Adding more hidden layers. * Using different activation functions for the hidden layers. * Adding or reducing the number of epochs to the training regimen. **NOTE**: You will not lose points if your model does not achieve target performance, as long as you make three attempts at optimizing the model in your jupyter notebook. 1. Create a new Jupyter Notebook file and name it `AlphabetSoupCharity_Optimzation.ipynb`. 2. Import your dependencies, and read in the `charity_data.csv` to a Pandas DataFrame. 3. Preprocess the dataset like you did in Step 1, taking into account any modifications to optimize the model. 4. Design a neural network model, taking into account any modifications that will optimize the model to achieve higher than 75% accuracy. 5. Save and export your results to an HDF5 file, and name it `AlphabetSoupCharity_Optimization.h5`. ### Step 4: Write a Report on the Neural Network Model For this part of the Challenge, you’ll write a report on the performance of the deep learning model you created for AlphabetSoup. The report should contain the following: 1. **Overview** of the analysis: Explain the purpose of this analysis. 2. **Results**: Using bulleted lists and images to support your answers, address the following questions. * Data Preprocessing * What variable(s) are considered the target(s) for your model? * What variable(s) are considered to be the features for your model? * What variable(s) are neither targets nor features, and should be removed from the input data? * Compiling, Training, and Evaluating the Model * How many neurons, layers, and activation functions did you select for your neural network model, and why? * Were you able to achieve the target model performance? * What steps did you take to try and increase model performance? 3. **Summary**: Summarize the overall results of the deep learning model. Include a recommendation for how a different model could solve this classification problem, and explain your recommendation.