Logistic Regression With Insurance Data

Last Updated on May 3, 2021

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To know the category which buy most insurance

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Air Quality Analysis And Prediction Of Italian City

Last Updated on May 3, 2021

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Problem statement

  • Predict
  • The value of CO in mg/m^3 reference value with respect to the available data. Please assume if you need, but do specify the same.
  • Forecast
  • The value pf CO in mg/m^3 for the next 3 3 weeks on hourly averaged concentration

Data Set Information

located on the field in a significantly polluted area, at road level,within an Italian city. Data were recorded from March 2004 to February 2005 (one year)representing the longest freely available recordings of on field deployed air quality chemical sensor devices responses. Ground Truth hourly averaged concentrations for CO, Non Metanic Hydrocarbons, Benzene, Total Nitrogen Oxides (NOx) and Nitrogen Dioxide (NO2) and were provided by a co-located reference certified analyzer. Evidences of cross-sensitivities as well as both concept and sensor drifts are present as described in De Vito et al., Sens. And Act. B, Vol. 129,2,2008 (citation required) eventually affecting sensors concentration

Data collection

0 Date (DD/MM/YYYY)

1 Time (HH.MM.SS)

2 True hourly averaged concentration CO in mg/m^3 (reference analyzer)

3 PT08.S1 (tin oxide) hourly averaged sensor response (nominally CO targeted)

4 True hourly averaged overall Non Metanic HydroCarbons concentration in microg/m^3 (reference analyzer)

5 True hourly averaged Benzene concentration in microg/m^3 (reference analyzer)

6 PT08.S2 (titania) hourly averaged sensor response (nominally NMHC targeted)

7 True hourly averaged NOx concentration in ppb (reference analyzer)

8 PT08.S3 (tungsten oxide) hourly averaged sensor response (nominally NOx targeted)

9 True hourly averaged NO2 concentration in microg/m^3 (reference analyzer)

10 PT08.S4 (tungsten oxide) hourly averaged sensor response (nominally NO2 targeted)

11 PT08.S5 (indium oxide) hourly averaged sensor response (nominally O3 targeted)

12 Temperature in °C

13 Relative Humidity (%)

14 AH Absolute Humidity.

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Smart Bag Tracker

Last Updated on May 3, 2021

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Smart bag is an application-specific design that can be useful for almost everyone in the

society. The loss or mishandling of luggage in airports is increasing nowadays,

tremendously raising its associated costs. It is expected that the constant monitoring

detects possible errors in a timely manner, allowing a proactive attitude when correcting

this kind of situations. There are several devices in the market but all have some

problems such as power consumption, location, portability, etc. The current research

provides a novel idea to track the luggage in real time with the help of a microcontroller

system, which is wearable and handy. Using wireless communication techniques, the

proposed system has been designed.


The system consists of GPS module which will fetch the current latitude and longitude and

using advanced Wi-Fi enabled microcontroller which will connect to the 4G


hotspot internet and transmit the current location of the bag to the central server. Using an

Android App the user can view the current position of the bag in google maps.


There are a lot of applications to the luggage but all of them are not controlled from the luggage, instead the commands are sent from the mobile phone to the luggage via Machine to Machine communication. The mobile phone has a pre-installed application software with a pre-installed set of instructions. They wait for the user to send the commands. This can either be for tracking its location.




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Eda On Sample_Superstore

Last Updated on May 3, 2021

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In this project I have performed Exploratory Data Analysis on a Sample_Superstore Dataset and concluded some of the major key insights which helps the store to generate more revenue. I have used python programming language to perform operations on my dataset. Firstly I identify the missing values , null values and the outliers. I used the libraries to make my work more easier, some of them are pandas, numpy, matplotlib and seaborn . It is must to remove outliers from our dataset because if we don't it will effect our result and produce wrong observations. So to remove them I have used IQR i.e. Interquartile range . With the help of matplotlib and seaborn library I visualize some of my observations with the help of graphs and charts. I have used heatmap to define the co-relation between different features present in our dataset which gives us a brief idea about how one feature is related to other features. I have used different different segments from my dataset for performing analysis and I have concluded some of the parameters like which segment is producing highest profit, loss , discount . Not just finding out the problems I have also mentioned some of the solutions by observing my result in order to increase the profit gain and to reduce the losses faced by the the store. I have performed detailed analysis on this dataset and All these observations that I have performed will definitely help the store to overcome with the problems


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Dice Simulator

Last Updated on May 3, 2021

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Python offers various packages to design the GUI, i.e. the Graphical User Interface. Tkinter is the most common, fast, and easy to use Python package used to build Graphical User Interface applications. It provides a powerful Object-Oriented Interface and is easy to use. Also, you develop an application; you can use it on any platform, which reduces the need of amendments required to use an app on Windows, Mac, or Linux.

It’s a simple cube with numbers from 1 to 6 written on its face. The simulation is the making of computer model. Thus, a dice simulator is a simple computer model that can roll a dice for us.


The first step is importing the required module where we import Tkinter which is used to make GUI applications and also the random module to generate random numbers.


The next step is Building a top-level widget to make the main window for our application here we will build the main window of our application, where the buttons, labels, and images will reside. We also give it a title by title() function.


The third step is designing the buttons:

Here, we use pack() to arrange our widgets in row and column form. The ‘BlankLine’ label is to skip a line, whereas we use ‘HeadingLabel’ label to give a heading.


The ‘rolling_dice’ function is a function that is executed every time a button is clicked. This is attained through the ‘command=rolling_dice’ parameter while defining a button.


Then ‘root.mainloop()’ is used to open the main window. It acts as the main function of our program.


We have successfully developed a cool application – Dice Rolling Simulator in Python. Now, you can just click on a button and get your next number.

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Web Base Application Heart Failure Prediction System

Last Updated on May 3, 2021

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In this situation, approximately 17 million people kill globally per year in the whole world because of cardiovascular disease, and they mainly exhibit myocardial-exhibit myocardial infarction and heart failure. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.

In this heart prediction problem statement, we are trying to predict whether the patient's heart muscle pumps blood properly or not using Logistic Regression. In this project, a dataset is downloaded from the UCI repository and this dataset is real. this dataset is collected from one of the most famous hospitals is in the United Kingdom (UK) in 2015 and there are 299 patient records and 12 features(attribute) and one label. Based on that 12 features, we will predict whether the patient's heart working properly or not.

In this problem statement, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning & classifiers to both predict the patient’s survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone.

For model building we use various library packages like Pandas, Scikit learns (sklearn), matplotlib, Seaborn, Tensorflow, Keras, etc., then we will use data description, Data description involves carrying out initial analysis on the data to understand more about the data, its source, volume, attributes, and relationships. Once these details are documented, any shortcomings if noted should be informed to relevant personnel. after that, we use the data cleaning method for cleaning the dataset to check if there are any missing values or not and we split the dataset into training & testing purposes with 70%, 30% criteria. Then the next step is Model Building, The process of model building is also known as training the model using data and features from our dataset. A combination of data (features) and Machine Learning algorithms together give us a model that tries to generalize on the training data and give necessary results in the form of insights and/or predictions. Generally, various algorithms are used to try out multiple modeling approaches on the same data to solve the same problem to get the best model that performs and gives outputs that are the closest to the business success criteria. Key things to keep track of here are the models created, model parameters being used, and their results. And the last step is to analyze the result in this step we check our model score or accuracy by using Confusion Matrix and Model Score. For this model, we got 80% accuracy. In the future, we try to improve that accuracy. For model deployment, we use the python flask and based on that we build the web-based application.


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