Logistic Regression - Project

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Logistic Regression Project

In this project I will be working with a fake advertising data set, indicating whether or not a particular internet user clicked on an Advertisement. I will try to create a model that will predict whether or not they will click on an ad based off the features of that user.
This data set contains the following features:

  • 'Daily Time Spent on Site': consumer time on site in minutes
  • 'Age': cutomer age in years
  • 'Area Income': Avg. Income of geographical area of consumer
  • 'Daily Internet Usage': Avg. minutes a day consumer is on the internet
  • 'Ad Topic Line': Headline of the advertisement
  • 'City': City of consumer
  • 'Male': Whether or not consumer was male
  • 'Country': Country of consumer
  • 'Timestamp': Time at which consumer clicked on Ad or closed window
  • 'Clicked on Ad': 0 or 1 indicated clicking on Ad


Import Libraries
     
First of all, import the libraries



Get the Data

Then get the data


Next, check information of data


Exploratory Data Analysis


I'll use seaborn to explore the data











Logistic Regression
now, it's time to do a train test split

Predictions and Evaluations

Conclusion

if we hold all other feature fixed the result of prediction would correct around ~90%

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Wine Quality Prediction(Logistic Regression) - Kaggle[Dataset]

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Overview from kaggle Context The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. For more details, consult the reference [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.). These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones). This dataset is also available from the UCI machine learning repository,  https://archive.ics.uci.edu/ml/datasets/wine+quality  , I just shared it to kaggle for convenience. (If I am mistaken and the public license type disallowed me from doing so, I will take this down if requested.) Content For more information, read [Cortez et al., 2009]. Input variables (based on physicochemical tests): 1 - fixed acidity
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Titanic - Kaggle

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Competition Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy. Practice Skills Binary classification Python and R basics And here is a result I use logi
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K Nearest Neighbors - Project

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K Nearest Neighbors Project I've been given a classified data set from a company(fake data again..). It has hidden the feature column names but has given the data and the target classes. I'll try to use KNN to create a model that directly predicts a class for a new data point based off of the features. Import Libraries I'll import libraries that I need to use as always Get the Data Exploratory Data Analysis ¶ Since this data is artificial, I'll just do a large pairplot with seaborn. Standardize the Variables ¶ Time to standardize the variables. Train Test Split Using KNN Predictions and Evaluations Choosing a K Value after I saw accuracy is quite low, so I will pick a new K value. Retrain with new K Value   after I saw K value around 25-28 are quite okay, so I will pick K value at 27
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