Please find questions for this assignment in the document. You need to complete all those questions...

Question

Please find questions for this assignment in the document. You need to complete all those questions including short answers in documentation and script for coding. Thank you.

For the following assignments, please provide as much evidence of the results as possible, including the code, screenshots (only plots – not text or code) and documentation. Submit only one pdf file and .ipynb / .py files containing the code with documentation. Choose any cleaned dataset such as the ones here: https://www.kaggle.com/search?q=cleaned+datasets+datasetFileTypes%3Acsv 1.a. [10 points] Ignore the label column and apply the AgglomerativeClustering method from sklearn.cluster on this dataset. Use min, average, and ward methods explained in the class to perform the hierarchical clustering. Please feel free to refer to https://scikit-learn.org/stable/auto_examples/cluster/plot_digits_linkage.html#sphx-glr-auto-examples-cluster-plot-digits-linkage-py 1.b. [10 points] Generate visualizations like in the above tutorial and dendrograms (please feel free to refer https://scikit-learn.org/stable/search.html?q=dendrogram) for each of the methods. 1.c. [10 points] Which method produces clusters that are most closely aligned with the labels in the dataset? Explain. 1.d. [20 points] Using the k-means algorithm where k=2 and corresponding visualizations, explain if it fares better than the agglomerative approaches in terms of the alignment with the labels. Hint: (a) Choose a smaller dataset for easier and better visualization and analysis (b) Cut the dendrogram at an appropriate level to result in just two clusters, in order to see how aligned these two clusters are with the assigned labels. 2. [25 points] The wine data set at https://archive.ics.uci.edu/ml/datasets/wine has 13 features. Develop in Python and apply your own version of the PCA algorithm to this data set, to visualize how PCA helps with dimensionality reduction. Explain how many Principal Components you will choose and why. What percent of the variance in the data do the selected Principal Components cover? For the implementation, you may use any objects, modules, and functions in NumPy, SciPy and other python libraries to do various operations such as to compute the eigen values, vectors or perform any other math / linear algebra operation, but not use the PCA function available in SciKit-Learn directly. 3.a. [20 points] Refer to online tutorials on regularization such as https://medium.com/coinmonks/regularization-of-linear-models-with-sklearn-f88633a93a2 and https://towardsdatascience.com/ridge-and-lasso-regression-a-complete-guide-with-python-scikit-learn-e20e34bcbf0b Apply the techniques from the above tutorial to the student dataset at https://archive.ics.uci.edu/ml/datasets/student+performance Does regularization help improve the accuracy of predicting the final Math grade of the students? 3.b. [5 points] For regularization, we added the regularizer to the loss function. Does it make sense to multiply or subtract the term, instead? Explain.

instructions-451qapd3.docx

Suraj · Accepted Answer

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd
",
    "import numpy as np
",
    "import matplotlib.pyplot as plt
",
    "from sklearn.cluster import AgglomerativeClustering
",
    "import scipy.cluster.hierarchy as shc
",
    "from sklearn.metrics import silhouette_score
",
    "from sklearn.cluster import KMeans
",
    "from sklearn.preprocessing import StandardScaler
",
    "from sklearn.decomposition import PCA
",
    "from sklearn.linear_model import LinearRegression
",
    "from sklearn.metrics import mean_squared_error
",
    "import math
",
    "from sklearn.linear_model import Ridge
",
    "from sklearn.preprocessing import PolynomialFeatures
",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }
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       "    
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       "      
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       "      CustomerID
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       "      Genre
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       "      Age
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       "      Annual Income (k$)
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       "      Spending Score (1-100)
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       "    
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       "      0
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       "      Female
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       "      17
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       "      40
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      ],
      "text/plain": [
       "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)
",
       "0           1    Male   19                  15                      39
",
       "1           2    Male   21                  15                      81
",
       "2           3  Female   20                  16                       6
",
       "3           4  Female   23                  16                      77
",
       "4           5  Female   31                  17                      40"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv("C:/Users/Hp/Desktop/Mall_Customers.csv")
",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=df.iloc[:,3:4].values
",
    "# dendrogram
",
    "
",
    "plt.figure(figsize =(8, 8))
",
    "plt.title('Visualising the data')
",
    "Dendrogram = shc.dendrogram((shc.linkage(x, method ='single')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1=AgglomerativeClustering(n_clusters=3,affinity="euclidean",linkage="single")
",
    "pred=model1.fit_predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(6, 6))
",
    "plt.scatter(df['Annual Income (k$)'], df['Spending Score (1-100)'], 
",
    "           c = model1.fit_predict(x), cmap ='rainbow')
",
    "plt.title("Using Min method")
",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png":

For the following assignments, please provide as much evidence of the results as possible, including the code, screenshots (only plots – not text or code) and documentation. Submit only one pdf file...

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	CustomerID	Genre	Age	Annual Income (k$)	Spending Score (1-100)
0	1	Male	19	15	39
1	2	Male	21	15	81
2	3	Female	20	16	6
3	4	Female	23	16	77
4	5	Female	31	17	40