/ML-Project-Decision-Tree

This Github repository contains projects related to prediction with Decision Tree. Exploring Insights/Inferences by performing EDA on the given project data (Iphone purchase).

Primary LanguageJupyter Notebook

Prediction with Decision Tree

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.

Python Libraries Used:

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

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

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

Contribution

Still Learning,

So feel free, Anything You wanna contirubute.