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Data Visualization of 911 Calls

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Data Visualization of 911 Calls Introduction In this project, I have analyzed some 911 call data from Kaggle . The data contains the following fields: lat : String variable, Latitude lng: String variable, Longitude desc: String variable, Description of the Emergency Call zip: String variable, Zipcode title: String variable, Title timeStamp: String variable, YYYY-MM-DD HH:MM:SS twp: String variable, Township addr: String variable, Address e: String variable, Dummy variable (always 1) First, we will have to make some changes in the dataset so we can perform the visualizations.  Creating a New Feature In the titles column, there are "Reasons/Departments" specified before the title code. These are EMS, Fire, and Traffic. By using  .apply() with a custom lambda expression I have created a new column called "Reason" that contains this string value. For example, if the title column value is EMS: BACK PAINS/INJURY, the Reason column value woul
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Recognising Handwritten Digits

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Recognizing  Handwritten Digits In this project, I have uploaded the data-set and tested  for the hypothesis which is " The Digits data set of the sci-kit-learn library provides numerous data-sets.Some Scientist claims that it predicts the digit accurately 95% of the time.  I performed data analysis to accept or reject this Hypothesis  and  finally put-forth my conclusion.  The sci-kit-learn library (HTTP://scikit-learn.org/) enables helps you to approach this type of project. The data to be analyzed is closely related to numerical values or strings, but can also involve images and sounds. The problem  faced in this  project is to  predict a numeric value, and then reading and interpreting an image that uses a handwritten font. So even in this case I  have used an estimator with the task of learning through a fit() function, and once it has reached a degree of predictive capability (a model sufficiently valid), which will produce a prediction with the predict() functio
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Performing Analysis of Meteorological Data

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Performing Analysis of Meteorological Data In this project, I have found meteorological data for 10 cities of Maharashtra State. Meteorological  parameters such as pressure, temperature, humidity, rain, etc.  I  have cleaned the data set for analysis, performed data cleaning, performed analysis for tested for the Hypothesis which is the effect  the proximity of the sea has on the climate, and  finally put-forth my conclusion.  this is the following steps I have performed:   Choose a set of 10 cities that will serve as reference standards. These cities are selected in order to cover the entire range of the plain.  Having distances of 0 km to up-to 400 km from the sea. ( Easiest way is to approximate the distance of (South) Mumbai as 0 km, as we all know it's touching the Arabian sea. Hence you can find all cities up-to 400 km, from Mumbai.)    Now we have to determine the distances of these cities from the sea(I have used google maps to determine the distance between th
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