Prediction with Decision Tree

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Projects:

Predicting the purchase of Iphones on the basis of gender, age and salary.

Objectives:

Exploring Insights/Inferences by performing EDA on the given data. Relevant graphs were plotted to get some insights on data using seaborn package. Model fitting via Decision Tree by Importing sklearn package.

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Python Libraries Used:

• Pandas
• Numpy
• Matplotlib
• Seaborn
• Scikit learn
• Joblib

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Methodology:

1. Data copying and cleaning:

   • Read the csv file
   • copy the data
   • check for null values and other informations
   • drop the duplicate values

2. Exploratory Data Analysis:

   • Conduct all the necessary EDA using various graphs on the dataset
   • interpret the graphs
   • check for outliers and correlation among the coloumns
   • perform one hot encoding in case of categorical columns

3. Sampling of data:

   • Divide the data into x and y
   • standardize the data using StandardScaler lib
   • import test_train_split from sklearn.model_selection
   • divide the data into training and testing

4. Modelling of data:

   • import DecisionTreeClassifier and initialize it
   • fit the model
   • predict the model

5. Model validation (Error Calculation):

   • From sklearn.metris import accuracy_score, precision_score, confusion_matrix
   • check the accuracy of the model

6. Save the Model:

   • import joblib
   • save the model

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Iphone Purchase data Probelm:

File name: Iphone-purchase-data files

EDA Inferences:

• There are total 198 and 182 Females and Males, respectively.
• Out of 198 females, 77 has purchased Iphones while out of 182 males, 63 has purchased Iphones.
• Females have purchased more Iphones compared to males.
• Genders with more salary are likely to purchase Iphones.
• Females have more salaries and hence purchased more Iphones than males.
• Genders with high salaries in 27-45 years of age have purchased Iphones.
• Genders having age group above 45 years purchased Iphones irrespective of their salaries.
• The data is normally distributed in all the cases. The skewness is calculted to be 0.23, 0.46 and 0.54 for age, salary and purchase Iphones, respectively.
• The correlation of age and salary with the Iphone purchase is 62 and 38%, repectively.
• There are not outliers present in the data.

Decision Tree Model Results:

The accuracy of the model is came out to be:

Accuracy Score: 0.92

Precision Score:0.92

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Contribution

Still Learning,

So feel free, Anything You wanna contirubute.

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