Linear Regression - Project

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E commerce company based in New York City that sells clothing online but they also have in-store style and clothing advice sessions. 



Customers come in to the store, have sessions/meetings with a personal stylist, then they can go home and order either on a mobile app or website for the clothes they want.

The company is trying to decide whether to focus their efforts on their mobile app experience or their website.(it's fake data. it's not real credit card numbers or emails)

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 GET THE DATA 



First of all I will import libraries that I need to use in this project, then I will import data set to my jupyter notebook.

Next, I will Check the head of customers, and check out its info() and describe() methods





Exploratory Data Analysis

I will use seaborn to create a jointplot to compare the Time on Website and Yearly Amount Spent columns. Does the correlation make sense?


Next, I will find relationships across the entire data set by using pairplot

Based off this plot it looks like Length of Membership is the most correlated feature with Yearly Amount Spent


Training and Testing Data

Now that I've explored the data a bit, let's go ahead and split the data into training and testing sets. Set a variable X equal to
 the numerical features of the customers and a variable y equal to the "Yearly Amount Spent" column. 







Predicting Test Data


Now that I have fit our model, let's evaluate its performance by predicting off the test values

Evaluating the Model

Let's evaluate our model performance by calculating the residual sum of squares and the explained variance score (R^2).



Conclusion

I still want to figure out the answer to the original question, do I focus our effort on mobile app or website development? 
Or maybe that doesn't even really matter?

Interpreting the coefficients
- holding all other features fixed, a 1 unit increase in Avg. Session Length is associated with an increase of 25.98
 total dollars spent.
- Holding all other features fixed, a 1 unit increase in Time on App is associated with an increase of 38.59 
total dollars spent.
- Holding all other features fixed, a 1 unit increase in Time on Website is associated with an increase of 0.19
 total dollars spent.
- Holding all other features fixed, a 1 unit increase in Length of Membership is associated with an increase of 61.27 
total dollars spent.
So, there are two ways to think about this: Develop the Website to catch up to the performance of the mobile app,
 or develop the app more since that is what is working better.

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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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