├── StudentPerformance.csv
├── README.md
├── Iris.csv
├── Covid-Vaccine.ipynb
├── Mall_Customers.csv
├── Social_Network_Ads.csv
├── MovieRecommendation.ipynb
├── Assignment_7.ipynb
├── BostonHousing.csv
├── Assignment_9.ipynb
└── Assignment_6.ipynb


/StudentPerformance.csv:
--------------------------------------------------------------------------------
 1 | gender,math_score,reading_score,writing_score,placement_score,club_join_year,placement_offer
 2 | female,63,84,64,84,2020,2
 3 | female,71,80,76,86,2018,3
 4 | female,64,81,66,81,2020,2
 5 | male,71,85,77,96,2018,1
 6 | male,68,86,76,,2021,3
 7 | female,94,86,61,100,2019,1
 8 | male,75,79,66,-99,2020,1
 9 | female,,,66,95,2019,3
10 | male,66,88,66,88,2020,3
11 | male,70,79,61,87,2021,2
12 | female,-99,80,65,85,2021,1
13 | male,76,84,-99,,2020,2
14 | female,74,79,79,98,2019,2
15 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Third Year Data Science And Big Data Analysis Lab Practicals
 2 | 
 3 | ## Assignment 1 - Data Wrangling Part 1
 4 | -  [Access here](https://github.com/Shubham-Bhoite/DSBDA-Assignment/blob/main/Assignment1.ipynb)
 5 | 
 6 | 
 7 | ## Assignment 2 - Data Wrangling Part 2
 8 | -  [Access here](https://github.com/Shubham-Bhoite/DSBDA-Assignment/blob/main/Assignment2.ipynb)
 9 | 
10 | 
11 | ## Assignment 3 - Statistics Operations
12 | -  [Access here](https://github.com/Shubham-Bhoite/DSBDA-Assignment/blob/main/Assignment_3.ipynb)
13 | 
14 | 
15 | ## Assignment 4 - Linear Regression Model
16 | -  [Access here](https://github.com/Shubham-Bhoite/DSBDA-Assignment/blob/main/Assignment4.ipynb)
17 | 
18 | 
19 | ## Assignment 5 - Logistic Regression
20 | -  [Access here](https://github.com/Shubham-Bhoite/DSBDA-Assignment/blob/main/Assignment%205_1.ipynb)
21 | 
22 | 
23 | ## Assignment 6 - Naïve Bayes Classification Algorithm
24 | -  [Access here](https://github.com/Shubham-Bhoite/DSBDA-Assignment/blob/main/Assignment_6.ipynb)
25 | 
26 | 
27 | ## Assignment 7 - Text Analytics
28 | -   [Access here](https://github.com/Shubham-Bhoite/DSBDA-Assignment/blob/main/Assignment_7.ipynb)
29 | 
30 | 
31 | ## Assignment 8 - Data Visualization-I
32 | -   [Access here](https://github.com/Shubham-Bhoite/DSBDA-Assignment/blob/main/Assignment_8.ipynb)
33 | 
34 | 
35 | ## Assignment 9 - Data Visualization II
36 | -  [Access here](https://github.com/Shubham-Bhoite/DSBDA-Assignment/blob/main/Assignment_9.ipynb)
37 | 
38 | 
39 | ## Assignment 10 - Data Visualization III
40 | -  [Access here](https://github.com/Shubham-Bhoite/DSBDA-Assignment/blob/main/Assignment%2010.ipynb)
41 | 
42 | 
43 | 
44 | # Mini Projects:
45 |  ## Project 1 - Covid Data Analytics
46 | -   [Access here](https://github.com/Shubham-Bhoite/DSBDA-Assignment/blob/main/Covid-Vaccine.ipynb)
47 | 
48 |  ## Project 2 - Movie Recommendation System
49 |  -  [Access here](https://github.com/Shubham-Bhoite/DSBDA-Assignment/blob/main/MovieRecommendation.ipynb)
50 | 


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/Iris.csv:
--------------------------------------------------------------------------------
  1 | "sepal.length","sepal.width","petal.length","petal.width","variety"
  2 | 5.1,3.5,1.4,.2,"Setosa"
  3 | 4.9,3,1.4,.2,"Setosa"
  4 | 4.7,3.2,1.3,.2,"Setosa"
  5 | 4.6,3.1,1.5,.2,"Setosa"
  6 | 5,3.6,1.4,.2,"Setosa"
  7 | 5.4,3.9,1.7,.4,"Setosa"
  8 | 4.6,3.4,1.4,.3,"Setosa"
  9 | 5,3.4,1.5,.2,"Setosa"
 10 | 4.4,2.9,1.4,.2,"Setosa"
 11 | 4.9,3.1,1.5,.1,"Setosa"
 12 | 5.4,3.7,1.5,.2,"Setosa"
 13 | 4.8,3.4,1.6,.2,"Setosa"
 14 | 4.8,3,1.4,.1,"Setosa"
 15 | 4.3,3,1.1,.1,"Setosa"
 16 | 5.8,4,1.2,.2,"Setosa"
 17 | 5.7,4.4,1.5,.4,"Setosa"
 18 | 5.4,3.9,1.3,.4,"Setosa"
 19 | 5.1,3.5,1.4,.3,"Setosa"
 20 | 5.7,3.8,1.7,.3,"Setosa"
 21 | 5.1,3.8,1.5,.3,"Setosa"
 22 | 5.4,3.4,1.7,.2,"Setosa"
 23 | 5.1,3.7,1.5,.4,"Setosa"
 24 | 4.6,3.6,1,.2,"Setosa"
 25 | 5.1,3.3,1.7,.5,"Setosa"
 26 | 4.8,3.4,1.9,.2,"Setosa"
 27 | 5,3,1.6,.2,"Setosa"
 28 | 5,3.4,1.6,.4,"Setosa"
 29 | 5.2,3.5,1.5,.2,"Setosa"
 30 | 5.2,3.4,1.4,.2,"Setosa"
 31 | 4.7,3.2,1.6,.2,"Setosa"
 32 | 4.8,3.1,1.6,.2,"Setosa"
 33 | 5.4,3.4,1.5,.4,"Setosa"
 34 | 5.2,4.1,1.5,.1,"Setosa"
 35 | 5.5,4.2,1.4,.2,"Setosa"
 36 | 4.9,3.1,1.5,.2,"Setosa"
 37 | 5,3.2,1.2,.2,"Setosa"
 38 | 5.5,3.5,1.3,.2,"Setosa"
 39 | 4.9,3.6,1.4,.1,"Setosa"
 40 | 4.4,3,1.3,.2,"Setosa"
 41 | 5.1,3.4,1.5,.2,"Setosa"
 42 | 5,3.5,1.3,.3,"Setosa"
 43 | 4.5,2.3,1.3,.3,"Setosa"
 44 | 4.4,3.2,1.3,.2,"Setosa"
 45 | 5,3.5,1.6,.6,"Setosa"
 46 | 5.1,3.8,1.9,.4,"Setosa"
 47 | 4.8,3,1.4,.3,"Setosa"
 48 | 5.1,3.8,1.6,.2,"Setosa"
 49 | 4.6,3.2,1.4,.2,"Setosa"
 50 | 5.3,3.7,1.5,.2,"Setosa"
 51 | 5,3.3,1.4,.2,"Setosa"
 52 | 7,3.2,4.7,1.4,"Versicolor"
 53 | 6.4,3.2,4.5,1.5,"Versicolor"
 54 | 6.9,3.1,4.9,1.5,"Versicolor"
 55 | 5.5,2.3,4,1.3,"Versicolor"
 56 | 6.5,2.8,4.6,1.5,"Versicolor"
 57 | 5.7,2.8,4.5,1.3,"Versicolor"
 58 | 6.3,3.3,4.7,1.6,"Versicolor"
 59 | 4.9,2.4,3.3,1,"Versicolor"
 60 | 6.6,2.9,4.6,1.3,"Versicolor"
 61 | 5.2,2.7,3.9,1.4,"Versicolor"
 62 | 5,2,3.5,1,"Versicolor"
 63 | 5.9,3,4.2,1.5,"Versicolor"
 64 | 6,2.2,4,1,"Versicolor"
 65 | 6.1,2.9,4.7,1.4,"Versicolor"
 66 | 5.6,2.9,3.6,1.3,"Versicolor"
 67 | 6.7,3.1,4.4,1.4,"Versicolor"
 68 | 5.6,3,4.5,1.5,"Versicolor"
 69 | 5.8,2.7,4.1,1,"Versicolor"
 70 | 6.2,2.2,4.5,1.5,"Versicolor"
 71 | 5.6,2.5,3.9,1.1,"Versicolor"
 72 | 5.9,3.2,4.8,1.8,"Versicolor"
 73 | 6.1,2.8,4,1.3,"Versicolor"
 74 | 6.3,2.5,4.9,1.5,"Versicolor"
 75 | 6.1,2.8,4.7,1.2,"Versicolor"
 76 | 6.4,2.9,4.3,1.3,"Versicolor"
 77 | 6.6,3,4.4,1.4,"Versicolor"
 78 | 6.8,2.8,4.8,1.4,"Versicolor"
 79 | 6.7,3,5,1.7,"Versicolor"
 80 | 6,2.9,4.5,1.5,"Versicolor"
 81 | 5.7,2.6,3.5,1,"Versicolor"
 82 | 5.5,2.4,3.8,1.1,"Versicolor"
 83 | 5.5,2.4,3.7,1,"Versicolor"
 84 | 5.8,2.7,3.9,1.2,"Versicolor"
 85 | 6,2.7,5.1,1.6,"Versicolor"
 86 | 5.4,3,4.5,1.5,"Versicolor"
 87 | 6,3.4,4.5,1.6,"Versicolor"
 88 | 6.7,3.1,4.7,1.5,"Versicolor"
 89 | 6.3,2.3,4.4,1.3,"Versicolor"
 90 | 5.6,3,4.1,1.3,"Versicolor"
 91 | 5.5,2.5,4,1.3,"Versicolor"
 92 | 5.5,2.6,4.4,1.2,"Versicolor"
 93 | 6.1,3,4.6,1.4,"Versicolor"
 94 | 5.8,2.6,4,1.2,"Versicolor"
 95 | 5,2.3,3.3,1,"Versicolor"
 96 | 5.6,2.7,4.2,1.3,"Versicolor"
 97 | 5.7,3,4.2,1.2,"Versicolor"
 98 | 5.7,2.9,4.2,1.3,"Versicolor"
 99 | 6.2,2.9,4.3,1.3,"Versicolor"
100 | 5.1,2.5,3,1.1,"Versicolor"
101 | 5.7,2.8,4.1,1.3,"Versicolor"
102 | 6.3,3.3,6,2.5,"Virginica"
103 | 5.8,2.7,5.1,1.9,"Virginica"
104 | 7.1,3,5.9,2.1,"Virginica"
105 | 6.3,2.9,5.6,1.8,"Virginica"
106 | 6.5,3,5.8,2.2,"Virginica"
107 | 7.6,3,6.6,2.1,"Virginica"
108 | 4.9,2.5,4.5,1.7,"Virginica"
109 | 7.3,2.9,6.3,1.8,"Virginica"
110 | 6.7,2.5,5.8,1.8,"Virginica"
111 | 7.2,3.6,6.1,2.5,"Virginica"
112 | 6.5,3.2,5.1,2,"Virginica"
113 | 6.4,2.7,5.3,1.9,"Virginica"
114 | 6.8,3,5.5,2.1,"Virginica"
115 | 5.7,2.5,5,2,"Virginica"
116 | 5.8,2.8,5.1,2.4,"Virginica"
117 | 6.4,3.2,5.3,2.3,"Virginica"
118 | 6.5,3,5.5,1.8,"Virginica"
119 | 7.7,3.8,6.7,2.2,"Virginica"
120 | 7.7,2.6,6.9,2.3,"Virginica"
121 | 6,2.2,5,1.5,"Virginica"
122 | 6.9,3.2,5.7,2.3,"Virginica"
123 | 5.6,2.8,4.9,2,"Virginica"
124 | 7.7,2.8,6.7,2,"Virginica"
125 | 6.3,2.7,4.9,1.8,"Virginica"
126 | 6.7,3.3,5.7,2.1,"Virginica"
127 | 7.2,3.2,6,1.8,"Virginica"
128 | 6.2,2.8,4.8,1.8,"Virginica"
129 | 6.1,3,4.9,1.8,"Virginica"
130 | 6.4,2.8,5.6,2.1,"Virginica"
131 | 7.2,3,5.8,1.6,"Virginica"
132 | 7.4,2.8,6.1,1.9,"Virginica"
133 | 7.9,3.8,6.4,2,"Virginica"
134 | 6.4,2.8,5.6,2.2,"Virginica"
135 | 6.3,2.8,5.1,1.5,"Virginica"
136 | 6.1,2.6,5.6,1.4,"Virginica"
137 | 7.7,3,6.1,2.3,"Virginica"
138 | 6.3,3.4,5.6,2.4,"Virginica"
139 | 6.4,3.1,5.5,1.8,"Virginica"
140 | 6,3,4.8,1.8,"Virginica"
141 | 6.9,3.1,5.4,2.1,"Virginica"
142 | 6.7,3.1,5.6,2.4,"Virginica"
143 | 6.9,3.1,5.1,2.3,"Virginica"
144 | 5.8,2.7,5.1,1.9,"Virginica"
145 | 6.8,3.2,5.9,2.3,"Virginica"
146 | 6.7,3.3,5.7,2.5,"Virginica"
147 | 6.7,3,5.2,2.3,"Virginica"
148 | 6.3,2.5,5,1.9,"Virginica"
149 | 6.5,3,5.2,2,"Virginica"
150 | 6.2,3.4,5.4,2.3,"Virginica"
151 | 5.9,3,5.1,1.8,"Virginica"


--------------------------------------------------------------------------------
/Covid-Vaccine.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "###  Use the covid_vaccine_statewise.csv dataset and perform following analytics on the given dataset"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 18,
 13 |    "metadata": {},
 14 |    "outputs": [],
 15 |    "source": [
 16 |     "import numpy as np \n",
 17 |     "import pandas as pd\n",
 18 |     "import warnings \n",
 19 |     "warnings.filterwarnings(\"ignore\")\n"
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "code",
 24 |    "execution_count": null,
 25 |    "metadata": {},
 26 |    "outputs": [],
 27 |    "source": [
 28 |     "df=pd.read_csv(\"https://raw.githubusercontent.com/kunalnandre/TE-Laboratory-Practicals/main/Data%20Science%20and%20Big%20Data/Mini%20Project/covid_vaccine_statewise.csv\")\n"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "markdown",
 33 |    "metadata": {},
 34 |    "source": [
 35 |     "### Describe the dataset"
 36 |    ]
 37 |   },
 38 |   {
 39 |    "cell_type": "code",
 40 |    "execution_count": null,
 41 |    "metadata": {},
 42 |    "outputs": [],
 43 |    "source": [
 44 |     "df.describe()"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "markdown",
 49 |    "metadata": {},
 50 |    "source": [
 51 |     "### Number of persons state wise vaccinated for first dose in India"
 52 |    ]
 53 |   },
 54 |   {
 55 |    "cell_type": "code",
 56 |    "execution_count": null,
 57 |    "metadata": {},
 58 |    "outputs": [],
 59 |    "source": [
 60 |     "print(\"Number of persons state wise vaccinated for first dose in India\")\n",
 61 |     "first_dose = df.groupby('State')[['First Dose Administered']].sum()\n",
 62 |     "first_dose"
 63 |    ]
 64 |   },
 65 |   {
 66 |    "cell_type": "markdown",
 67 |    "metadata": {},
 68 |    "source": [
 69 |     "### Number of persons state wise vaccinated for first dose in India"
 70 |    ]
 71 |   },
 72 |   {
 73 |    "cell_type": "code",
 74 |    "execution_count": null,
 75 |    "metadata": {},
 76 |    "outputs": [],
 77 |    "source": [
 78 |     "print(\"Number of persons state wise vaccinated for second dose in India\")\n",
 79 |     "\n",
 80 |     "first_dose = df.groupby('State')[['Second Dose Administered']].sum()\n",
 81 |     "first_dose"
 82 |    ]
 83 |   },
 84 |   {
 85 |    "cell_type": "markdown",
 86 |    "metadata": {},
 87 |    "source": [
 88 |     "### Number of Males vaccinated"
 89 |    ]
 90 |   },
 91 |   {
 92 |    "cell_type": "code",
 93 |    "execution_count": null,
 94 |    "metadata": {},
 95 |    "outputs": [],
 96 |    "source": [
 97 |     "male = df[\"Male(Individuals Vaccinated)\"].sum()\n",
 98 |     "print(\"Number of Males vaccinated are\", int(male))"
 99 |    ]
100 |   },
101 |   {
102 |    "cell_type": "markdown",
103 |    "metadata": {},
104 |    "source": [
105 |     "### Number of females vaccinated"
106 |    ]
107 |   },
108 |   {
109 |    "cell_type": "code",
110 |    "execution_count": null,
111 |    "metadata": {},
112 |    "outputs": [],
113 |    "source": [
114 |     "female = df[\"Female(Individuals Vaccinated)\"].sum()\n",
115 |     "print(\"Number of females vaccinated are\", int(female))"
116 |    ]
117 |   },
118 |   {
119 |    "cell_type": "code",
120 |    "execution_count": null,
121 |    "metadata": {},
122 |    "outputs": [],
123 |    "source": [
124 |     "df.info()"
125 |    ]
126 |   },
127 |   {
128 |    "cell_type": "code",
129 |    "execution_count": null,
130 |    "metadata": {},
131 |    "outputs": [],
132 |    "source": [
133 |     "df.describe(include='object')"
134 |    ]
135 |   },
136 |   {
137 |    "cell_type": "code",
138 |    "execution_count": null,
139 |    "metadata": {},
140 |    "outputs": [],
141 |    "source": [
142 |     "df.shape"
143 |    ]
144 |   },
145 |   {
146 |    "cell_type": "code",
147 |    "execution_count": null,
148 |    "metadata": {},
149 |    "outputs": [],
150 |    "source": [
151 |     "df.head()"
152 |    ]
153 |   },
154 |   {
155 |    "cell_type": "code",
156 |    "execution_count": null,
157 |    "metadata": {},
158 |    "outputs": [],
159 |    "source": [
160 |     "df.tail()"
161 |    ]
162 |   },
163 |   {
164 |    "cell_type": "code",
165 |    "execution_count": null,
166 |    "metadata": {},
167 |    "outputs": [],
168 |    "source": []
169 |   },
170 |   {
171 |    "cell_type": "code",
172 |    "execution_count": null,
173 |    "metadata": {},
174 |    "outputs": [],
175 |    "source": []
176 |   }
177 |  ],
178 |  "metadata": {
179 |   "kernelspec": {
180 |    "display_name": "Python 3",
181 |    "language": "python",
182 |    "name": "python3"
183 |   },
184 |   "language_info": {
185 |    "codemirror_mode": {
186 |     "name": "ipython",
187 |     "version": 3
188 |    },
189 |    "file_extension": ".py",
190 |    "mimetype": "text/x-python",
191 |    "name": "python",
192 |    "nbconvert_exporter": "python",
193 |    "pygments_lexer": "ipython3",
194 |    "version": "3.6.4"
195 |   }
196 |  },
197 |  "nbformat": 4,
198 |  "nbformat_minor": 2
199 | }
200 | 


--------------------------------------------------------------------------------
/Mall_Customers.csv:
--------------------------------------------------------------------------------
  1 | CustomerID,Genre,Age Group,Age,Annual Income (k$),Spending Score (1-100)
  2 | 1,Male,Teen,19,15,39
  3 | 2,Male,Middle Age,21,15,81
  4 | 3,Female,Middle Age,20,16,6
  5 | 4,Female,Middle Age,23,16,77
  6 | 5,Female,Middle Age,31,17,40
  7 | 6,Female,Middle Age,22,17,76
  8 | 7,Female,Middle Age,35,18,6
  9 | 8,Female,Middle Age,23,18,94
 10 | 9,Male,Elder,64,19,3
 11 | 10,Female,Middle Age,30,19,72
 12 | 11,Male,Elder,67,19,14
 13 | 12,Female,Middle Age,35,19,99
 14 | 13,Female,Elder,58,20,15
 15 | 14,Female,Middle Age,24,20,77
 16 | 15,Male,Middle Age,37,20,13
 17 | 16,Male,Middle Age,22,20,79
 18 | 17,Female,Middle Age,35,21,35
 19 | 18,Male,Middle Age,20,21,66
 20 | 19,Male,Elder,52,23,29
 21 | 20,Female,Middle Age,35,23,98
 22 | 21,Male,Middle Age,35,24,35
 23 | 22,Male,Middle Age,25,24,73
 24 | 23,Female,Elder,46,25,5
 25 | 24,Male,Middle Age,31,25,73
 26 | 25,Female,Elder,54,28,14
 27 | 26,Male,Middle Age,29,28,82
 28 | 27,Female,Elder,45,28,32
 29 | 28,Male,Middle Age,35,28,61
 30 | 29,Female,Elder,40,29,31
 31 | 30,Female,Middle Age,23,29,87
 32 | 31,Male,Elder,60,30,4
 33 | 32,Female,Middle Age,21,30,73
 34 | 33,Male,Elder,53,33,4
 35 | 34,Male,Teen,18,33,92
 36 | 35,Female,Elder,49,33,14
 37 | 36,Female,Middle Age,21,33,81
 38 | 37,Female,Elder,42,34,17
 39 | 38,Female,Middle Age,30,34,73
 40 | 39,Female,Middle Age,36,37,26
 41 | 40,Female,Middle Age,20,37,75
 42 | 41,Female,Elder,65,38,35
 43 | 42,Male,Middle Age,24,38,92
 44 | 43,Male,Elder,48,39,36
 45 | 44,Female,Middle Age,31,39,61
 46 | 45,Female,Elder,49,39,28
 47 | 46,Female,Middle Age,24,39,65
 48 | 47,Female,Elder,50,40,55
 49 | 48,Female,Middle Age,27,40,47
 50 | 49,Female,Middle Age,29,40,42
 51 | 50,Female,Middle Age,31,40,42
 52 | 51,Female,Elder,49,42,52
 53 | 52,Male,Middle Age,33,42,60
 54 | 53,Female,Middle Age,31,43,54
 55 | 54,Male,Elder,59,43,60
 56 | 55,Female,Elder,50,43,45
 57 | 56,Male,Elder,47,43,41
 58 | 57,Female,Elder,51,44,50
 59 | 58,Male,Elder,69,44,46
 60 | 59,Female,Middle Age,27,46,51
 61 | 60,Male,Elder,53,46,46
 62 | 61,Male,Elder,70,46,56
 63 | 62,Male,Teen,19,46,55
 64 | 63,Female,Elder,67,47,52
 65 | 64,Female,Elder,54,47,59
 66 | 65,Male,Elder,63,48,51
 67 | 66,Male,Teen,18,48,59
 68 | 67,Female,Elder,43,48,50
 69 | 68,Female,Elder,68,48,48
 70 | 69,Male,Teen,19,48,59
 71 | 70,Female,Middle Age,32,48,47
 72 | 71,Male,Elder,70,49,55
 73 | 72,Female,Elder,47,49,42
 74 | 73,Female,Elder,60,50,49
 75 | 74,Female,Elder,60,50,56
 76 | 75,Male,Elder,59,54,47
 77 | 76,Male,Middle Age,26,54,54
 78 | 77,Female,Elder,45,54,53
 79 | 78,Male,Elder,40,54,48
 80 | 79,Female,Middle Age,23,54,52
 81 | 80,Female,Elder,49,54,42
 82 | 81,Male,Elder,57,54,51
 83 | 82,Male,Middle Age,38,54,55
 84 | 83,Male,Elder,67,54,41
 85 | 84,Female,Elder,46,54,44
 86 | 85,Female,Middle Age,21,54,57
 87 | 86,Male,Elder,48,54,46
 88 | 87,Female,Elder,55,57,58
 89 | 88,Female,Middle Age,22,57,55
 90 | 89,Female,Middle Age,34,58,60
 91 | 90,Female,Elder,50,58,46
 92 | 91,Female,Elder,68,59,55
 93 | 92,Male,Teen,18,59,41
 94 | 93,Male,Elder,48,60,49
 95 | 94,Female,Elder,40,60,40
 96 | 95,Female,Middle Age,32,60,42
 97 | 96,Male,Middle Age,24,60,52
 98 | 97,Female,Elder,47,60,47
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100 | 99,Male,Elder,48,61,42
101 | 100,Male,Middle Age,20,61,49
102 | 101,Female,Middle Age,23,62,41
103 | 102,Female,Elder,49,62,48
104 | 103,Male,Elder,67,62,59
105 | 104,Male,Middle Age,26,62,55
106 | 105,Male,Elder,49,62,56
107 | 106,Female,Middle Age,21,62,42
108 | 107,Female,Elder,66,63,50
109 | 108,Male,Elder,54,63,46
110 | 109,Male,Elder,68,63,43
111 | 110,Male,Elder,66,63,48
112 | 111,Male,Elder,65,63,52
113 | 112,Female,Teen,19,63,54
114 | 113,Female,Middle Age,38,64,42
115 | 114,Male,Teen,19,64,46
116 | 115,Female,Teen,18,65,48
117 | 116,Female,Teen,19,65,50
118 | 117,Female,Elder,63,65,43
119 | 118,Female,Elder,49,65,59
120 | 119,Female,Elder,51,67,43
121 | 120,Female,Elder,50,67,57
122 | 121,Male,Middle Age,27,67,56
123 | 122,Female,Middle Age,38,67,40
124 | 123,Female,Elder,40,69,58
125 | 124,Male,Middle Age,39,69,91
126 | 125,Female,Middle Age,23,70,29
127 | 126,Female,Middle Age,31,70,77
128 | 127,Male,Elder,43,71,35
129 | 128,Male,Elder,40,71,95
130 | 129,Male,Elder,59,71,11
131 | 130,Male,Middle Age,38,71,75
132 | 131,Male,Elder,47,71,9
133 | 132,Male,Middle Age,39,71,75
134 | 133,Female,Middle Age,25,72,34
135 | 134,Female,Middle Age,31,72,71
136 | 135,Male,Middle Age,20,73,5
137 | 136,Female,Middle Age,29,73,88
138 | 137,Female,Elder,44,73,7
139 | 138,Male,Middle Age,32,73,73
140 | 139,Male,Teen,19,74,10
141 | 140,Female,Middle Age,35,74,72
142 | 141,Female,Elder,57,75,5
143 | 142,Male,Middle Age,32,75,93
144 | 143,Female,Middle Age,28,76,40
145 | 144,Female,Middle Age,32,76,87
146 | 145,Male,Middle Age,25,77,12
147 | 146,Male,Middle Age,28,77,97
148 | 147,Male,Elder,48,77,36
149 | 148,Female,Middle Age,32,77,74
150 | 149,Female,Middle Age,34,78,22
151 | 150,Male,Middle Age,34,78,90
152 | 151,Male,Elder,43,78,17
153 | 152,Male,Middle Age,39,78,88
154 | 153,Female,Elder,44,78,20
155 | 154,Female,Middle Age,38,78,76
156 | 155,Female,Elder,47,78,16
157 | 156,Female,Middle Age,27,78,89
158 | 157,Male,Middle Age,37,78,1
159 | 158,Female,Middle Age,30,78,78
160 | 159,Male,Middle Age,34,78,1
161 | 160,Female,Middle Age,30,78,73
162 | 161,Female,Elder,56,79,35
163 | 162,Female,Middle Age,29,79,83
164 | 163,Male,Teen,19,81,5
165 | 164,Female,Middle Age,31,81,93
166 | 165,Male,Elder,50,85,26
167 | 166,Female,Middle Age,36,85,75
168 | 167,Male,Elder,42,86,20
169 | 168,Female,Middle Age,33,86,95
170 | 169,Female,Middle Age,36,87,27
171 | 170,Male,Middle Age,32,87,63
172 | 171,Male,Elder,40,87,13
173 | 172,Male,Middle Age,28,87,75
174 | 173,Male,Middle Age,36,87,10
175 | 174,Male,Middle Age,36,87,92
176 | 175,Female,Elder,52,88,13
177 | 176,Female,Middle Age,30,88,86
178 | 177,Male,Elder,58,88,15
179 | 178,Male,Middle Age,27,88,69
180 | 179,Male,Elder,59,93,14
181 | 180,Male,Middle Age,35,93,90
182 | 181,Female,Middle Age,37,97,32
183 | 182,Female,Middle Age,32,97,86
184 | 183,Male,Elder,46,98,15
185 | 184,Female,Middle Age,29,98,88
186 | 185,Female,Elder,41,99,39
187 | 186,Male,Middle Age,30,99,97
188 | 187,Female,Elder,54,101,24
189 | 188,Male,Middle Age,28,101,68
190 | 189,Female,Elder,41,103,17
191 | 190,Female,Middle Age,36,103,85
192 | 191,Female,Middle Age,34,103,23
193 | 192,Female,Middle Age,32,103,69
194 | 193,Male,Middle Age,33,113,8
195 | 194,Female,Middle Age,38,113,91
196 | 195,Female,Elder,47,120,16
197 | 196,Female,Middle Age,35,120,79
198 | 197,Female,Elder,45,126,28
199 | 198,Male,Middle Age,32,126,74
200 | 199,Male,Middle Age,32,137,18
201 | 200,Male,Middle Age,30,137,83
202 | 


--------------------------------------------------------------------------------
/Social_Network_Ads.csv:
--------------------------------------------------------------------------------
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  2 | 15624510,Male,19,19000,0
  3 | 15810944,Male,35,20000,0
  4 | 15668575,Female,26,43000,0
  5 | 15603246,Female,27,57000,0
  6 | 15804002,Male,19,76000,0
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  8 | 15598044,Female,27,84000,0
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102 | 15766289,Male,27,88000,0
103 | 15593014,Male,28,59000,0
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111 | 15697997,Female,38,80000,0
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153 | 15576219,Male,41,45000,0
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157 | 15610140,Female,31,15000,0
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160 | 15762605,Male,26,30000,0
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162 | 15744279,Male,32,100000,1
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183 | 15774727,Female,31,71000,0
184 | 15694288,Female,32,117000,1
185 | 15603319,Male,33,43000,0
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187 | 15814816,Male,31,66000,0
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384 | 15715622,Female,44,139000,1
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391 | 15668521,Female,48,35000,1
392 | 15807837,Male,48,33000,1
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394 | 15748589,Female,45,45000,1
395 | 15635893,Male,60,42000,1
396 | 15757632,Female,39,59000,0
397 | 15691863,Female,46,41000,1
398 | 15706071,Male,51,23000,1
399 | 15654296,Female,50,20000,1
400 | 15755018,Male,36,33000,0
401 | 15594041,Female,49,36000,1


--------------------------------------------------------------------------------
/MovieRecommendation.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "Develop a movie recommendation model using the scikit-learn library in python. "
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 1,
 13 |    "metadata": {},
 14 |    "outputs": [],
 15 |    "source": [
 16 |     "from sklearn.metrics.pairwise import cosine_similarity\n",
 17 |     "import pandas as pd\n",
 18 |     "import numpy as np\n",
 19 |     "from sklearn.feature_extraction.text import CountVectorizer\n",
 20 |     "from sklearn.metrics.pairwise import cosine_similarity"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 2,
 26 |    "metadata": {},
 27 |    "outputs": [],
 28 |    "source": [
 29 |     "df = pd.read_csv(\"https://raw.githubusercontent.com/rashida048/Some-NLP-Projects/master/movie_dataset.csv\")"
 30 |    ]
 31 |   },
 32 |   {
 33 |    "cell_type": "code",
 34 |    "execution_count": 3,
 35 |    "metadata": {},
 36 |    "outputs": [
 37 |     {
 38 |      "data": {
 39 |       "text/html": [
 40 |        "<div>\n",
 41 |        "<style scoped>\n",
 42 |        "    .dataframe tbody tr th:only-of-type {\n",
 43 |        "        vertical-align: middle;\n",
 44 |        "    }\n",
 45 |        "\n",
 46 |        "    .dataframe tbody tr th {\n",
 47 |        "        vertical-align: top;\n",
 48 |        "    }\n",
 49 |        "\n",
 50 |        "    .dataframe thead th {\n",
 51 |        "        text-align: right;\n",
 52 |        "    }\n",
 53 |        "</style>\n",
 54 |        "<table border=\"1\" class=\"dataframe\">\n",
 55 |        "  <thead>\n",
 56 |        "    <tr style=\"text-align: right;\">\n",
 57 |        "      <th></th>\n",
 58 |        "      <th>index</th>\n",
 59 |        "      <th>budget</th>\n",
 60 |        "      <th>genres</th>\n",
 61 |        "      <th>homepage</th>\n",
 62 |        "      <th>id</th>\n",
 63 |        "      <th>keywords</th>\n",
 64 |        "      <th>original_language</th>\n",
 65 |        "      <th>original_title</th>\n",
 66 |        "      <th>overview</th>\n",
 67 |        "      <th>popularity</th>\n",
 68 |        "      <th>...</th>\n",
 69 |        "      <th>runtime</th>\n",
 70 |        "      <th>spoken_languages</th>\n",
 71 |        "      <th>status</th>\n",
 72 |        "      <th>tagline</th>\n",
 73 |        "      <th>title</th>\n",
 74 |        "      <th>vote_average</th>\n",
 75 |        "      <th>vote_count</th>\n",
 76 |        "      <th>cast</th>\n",
 77 |        "      <th>crew</th>\n",
 78 |        "      <th>director</th>\n",
 79 |        "    </tr>\n",
 80 |        "  </thead>\n",
 81 |        "  <tbody>\n",
 82 |        "    <tr>\n",
 83 |        "      <th>0</th>\n",
 84 |        "      <td>0</td>\n",
 85 |        "      <td>237000000</td>\n",
 86 |        "      <td>Action Adventure Fantasy Science Fiction</td>\n",
 87 |        "      <td>http://www.avatarmovie.com/</td>\n",
 88 |        "      <td>19995</td>\n",
 89 |        "      <td>culture clash future space war space colony so...</td>\n",
 90 |        "      <td>en</td>\n",
 91 |        "      <td>Avatar</td>\n",
 92 |        "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
 93 |        "      <td>150.437577</td>\n",
 94 |        "      <td>...</td>\n",
 95 |        "      <td>162.0</td>\n",
 96 |        "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
 97 |        "      <td>Released</td>\n",
 98 |        "      <td>Enter the World of Pandora.</td>\n",
 99 |        "      <td>Avatar</td>\n",
100 |        "      <td>7.2</td>\n",
101 |        "      <td>11800</td>\n",
102 |        "      <td>Sam Worthington Zoe Saldana Sigourney Weaver S...</td>\n",
103 |        "      <td>[{'name': 'Stephen E. Rivkin', 'gender': 0, 'd...</td>\n",
104 |        "      <td>James Cameron</td>\n",
105 |        "    </tr>\n",
106 |        "    <tr>\n",
107 |        "      <th>1</th>\n",
108 |        "      <td>1</td>\n",
109 |        "      <td>300000000</td>\n",
110 |        "      <td>Adventure Fantasy Action</td>\n",
111 |        "      <td>http://disney.go.com/disneypictures/pirates/</td>\n",
112 |        "      <td>285</td>\n",
113 |        "      <td>ocean drug abuse exotic island east india trad...</td>\n",
114 |        "      <td>en</td>\n",
115 |        "      <td>Pirates of the Caribbean: At World's End</td>\n",
116 |        "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
117 |        "      <td>139.082615</td>\n",
118 |        "      <td>...</td>\n",
119 |        "      <td>169.0</td>\n",
120 |        "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
121 |        "      <td>Released</td>\n",
122 |        "      <td>At the end of the world, the adventure begins.</td>\n",
123 |        "      <td>Pirates of the Caribbean: At World's End</td>\n",
124 |        "      <td>6.9</td>\n",
125 |        "      <td>4500</td>\n",
126 |        "      <td>Johnny Depp Orlando Bloom Keira Knightley Stel...</td>\n",
127 |        "      <td>[{'name': 'Dariusz Wolski', 'gender': 2, 'depa...</td>\n",
128 |        "      <td>Gore Verbinski</td>\n",
129 |        "    </tr>\n",
130 |        "    <tr>\n",
131 |        "      <th>2</th>\n",
132 |        "      <td>2</td>\n",
133 |        "      <td>245000000</td>\n",
134 |        "      <td>Action Adventure Crime</td>\n",
135 |        "      <td>http://www.sonypictures.com/movies/spectre/</td>\n",
136 |        "      <td>206647</td>\n",
137 |        "      <td>spy based on novel secret agent sequel mi6</td>\n",
138 |        "      <td>en</td>\n",
139 |        "      <td>Spectre</td>\n",
140 |        "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
141 |        "      <td>107.376788</td>\n",
142 |        "      <td>...</td>\n",
143 |        "      <td>148.0</td>\n",
144 |        "      <td>[{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...</td>\n",
145 |        "      <td>Released</td>\n",
146 |        "      <td>A Plan No One Escapes</td>\n",
147 |        "      <td>Spectre</td>\n",
148 |        "      <td>6.3</td>\n",
149 |        "      <td>4466</td>\n",
150 |        "      <td>Daniel Craig Christoph Waltz L\\u00e9a Seydoux ...</td>\n",
151 |        "      <td>[{'name': 'Thomas Newman', 'gender': 2, 'depar...</td>\n",
152 |        "      <td>Sam Mendes</td>\n",
153 |        "    </tr>\n",
154 |        "    <tr>\n",
155 |        "      <th>3</th>\n",
156 |        "      <td>3</td>\n",
157 |        "      <td>250000000</td>\n",
158 |        "      <td>Action Crime Drama Thriller</td>\n",
159 |        "      <td>http://www.thedarkknightrises.com/</td>\n",
160 |        "      <td>49026</td>\n",
161 |        "      <td>dc comics crime fighter terrorist secret ident...</td>\n",
162 |        "      <td>en</td>\n",
163 |        "      <td>The Dark Knight Rises</td>\n",
164 |        "      <td>Following the death of District Attorney Harve...</td>\n",
165 |        "      <td>112.312950</td>\n",
166 |        "      <td>...</td>\n",
167 |        "      <td>165.0</td>\n",
168 |        "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
169 |        "      <td>Released</td>\n",
170 |        "      <td>The Legend Ends</td>\n",
171 |        "      <td>The Dark Knight Rises</td>\n",
172 |        "      <td>7.6</td>\n",
173 |        "      <td>9106</td>\n",
174 |        "      <td>Christian Bale Michael Caine Gary Oldman Anne ...</td>\n",
175 |        "      <td>[{'name': 'Hans Zimmer', 'gender': 2, 'departm...</td>\n",
176 |        "      <td>Christopher Nolan</td>\n",
177 |        "    </tr>\n",
178 |        "    <tr>\n",
179 |        "      <th>4</th>\n",
180 |        "      <td>4</td>\n",
181 |        "      <td>260000000</td>\n",
182 |        "      <td>Action Adventure Science Fiction</td>\n",
183 |        "      <td>http://movies.disney.com/john-carter</td>\n",
184 |        "      <td>49529</td>\n",
185 |        "      <td>based on novel mars medallion space travel pri...</td>\n",
186 |        "      <td>en</td>\n",
187 |        "      <td>John Carter</td>\n",
188 |        "      <td>John Carter is a war-weary, former military ca...</td>\n",
189 |        "      <td>43.926995</td>\n",
190 |        "      <td>...</td>\n",
191 |        "      <td>132.0</td>\n",
192 |        "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
193 |        "      <td>Released</td>\n",
194 |        "      <td>Lost in our world, found in another.</td>\n",
195 |        "      <td>John Carter</td>\n",
196 |        "      <td>6.1</td>\n",
197 |        "      <td>2124</td>\n",
198 |        "      <td>Taylor Kitsch Lynn Collins Samantha Morton Wil...</td>\n",
199 |        "      <td>[{'name': 'Andrew Stanton', 'gender': 2, 'depa...</td>\n",
200 |        "      <td>Andrew Stanton</td>\n",
201 |        "    </tr>\n",
202 |        "  </tbody>\n",
203 |        "</table>\n",
204 |        "<p>5 rows × 24 columns</p>\n",
205 |        "</div>"
206 |       ],
207 |       "text/plain": [
208 |        "   index     budget                                    genres  \\\n",
209 |        "0      0  237000000  Action Adventure Fantasy Science Fiction   \n",
210 |        "1      1  300000000                  Adventure Fantasy Action   \n",
211 |        "2      2  245000000                    Action Adventure Crime   \n",
212 |        "3      3  250000000               Action Crime Drama Thriller   \n",
213 |        "4      4  260000000          Action Adventure Science Fiction   \n",
214 |        "\n",
215 |        "                                       homepage      id  \\\n",
216 |        "0                   http://www.avatarmovie.com/   19995   \n",
217 |        "1  http://disney.go.com/disneypictures/pirates/     285   \n",
218 |        "2   http://www.sonypictures.com/movies/spectre/  206647   \n",
219 |        "3            http://www.thedarkknightrises.com/   49026   \n",
220 |        "4          http://movies.disney.com/john-carter   49529   \n",
221 |        "\n",
222 |        "                                            keywords original_language  \\\n",
223 |        "0  culture clash future space war space colony so...                en   \n",
224 |        "1  ocean drug abuse exotic island east india trad...                en   \n",
225 |        "2         spy based on novel secret agent sequel mi6                en   \n",
226 |        "3  dc comics crime fighter terrorist secret ident...                en   \n",
227 |        "4  based on novel mars medallion space travel pri...                en   \n",
228 |        "\n",
229 |        "                             original_title  \\\n",
230 |        "0                                    Avatar   \n",
231 |        "1  Pirates of the Caribbean: At World's End   \n",
232 |        "2                                   Spectre   \n",
233 |        "3                     The Dark Knight Rises   \n",
234 |        "4                               John Carter   \n",
235 |        "\n",
236 |        "                                            overview  popularity  \\\n",
237 |        "0  In the 22nd century, a paraplegic Marine is di...  150.437577   \n",
238 |        "1  Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
239 |        "2  A cryptic message from Bond’s past sends him o...  107.376788   \n",
240 |        "3  Following the death of District Attorney Harve...  112.312950   \n",
241 |        "4  John Carter is a war-weary, former military ca...   43.926995   \n",
242 |        "\n",
243 |        "         ...         runtime  \\\n",
244 |        "0        ...           162.0   \n",
245 |        "1        ...           169.0   \n",
246 |        "2        ...           148.0   \n",
247 |        "3        ...           165.0   \n",
248 |        "4        ...           132.0   \n",
249 |        "\n",
250 |        "                                    spoken_languages    status  \\\n",
251 |        "0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
252 |        "1           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
253 |        "2  [{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...  Released   \n",
254 |        "3           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
255 |        "4           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
256 |        "\n",
257 |        "                                          tagline  \\\n",
258 |        "0                     Enter the World of Pandora.   \n",
259 |        "1  At the end of the world, the adventure begins.   \n",
260 |        "2                           A Plan No One Escapes   \n",
261 |        "3                                 The Legend Ends   \n",
262 |        "4            Lost in our world, found in another.   \n",
263 |        "\n",
264 |        "                                      title vote_average vote_count  \\\n",
265 |        "0                                    Avatar          7.2      11800   \n",
266 |        "1  Pirates of the Caribbean: At World's End          6.9       4500   \n",
267 |        "2                                   Spectre          6.3       4466   \n",
268 |        "3                     The Dark Knight Rises          7.6       9106   \n",
269 |        "4                               John Carter          6.1       2124   \n",
270 |        "\n",
271 |        "                                                cast  \\\n",
272 |        "0  Sam Worthington Zoe Saldana Sigourney Weaver S...   \n",
273 |        "1  Johnny Depp Orlando Bloom Keira Knightley Stel...   \n",
274 |        "2  Daniel Craig Christoph Waltz L\\u00e9a Seydoux ...   \n",
275 |        "3  Christian Bale Michael Caine Gary Oldman Anne ...   \n",
276 |        "4  Taylor Kitsch Lynn Collins Samantha Morton Wil...   \n",
277 |        "\n",
278 |        "                                                crew           director  \n",
279 |        "0  [{'name': 'Stephen E. Rivkin', 'gender': 0, 'd...      James Cameron  \n",
280 |        "1  [{'name': 'Dariusz Wolski', 'gender': 2, 'depa...     Gore Verbinski  \n",
281 |        "2  [{'name': 'Thomas Newman', 'gender': 2, 'depar...         Sam Mendes  \n",
282 |        "3  [{'name': 'Hans Zimmer', 'gender': 2, 'departm...  Christopher Nolan  \n",
283 |        "4  [{'name': 'Andrew Stanton', 'gender': 2, 'depa...     Andrew Stanton  \n",
284 |        "\n",
285 |        "[5 rows x 24 columns]"
286 |       ]
287 |      },
288 |      "execution_count": 3,
289 |      "metadata": {},
290 |      "output_type": "execute_result"
291 |     }
292 |    ],
293 |    "source": [
294 |     "df.head()"
295 |    ]
296 |   },
297 |   {
298 |    "cell_type": "code",
299 |    "execution_count": 4,
300 |    "metadata": {},
301 |    "outputs": [],
302 |    "source": [
303 |     "features = ['keywords','cast','genres','director']"
304 |    ]
305 |   },
306 |   {
307 |    "cell_type": "code",
308 |    "execution_count": 5,
309 |    "metadata": {},
310 |    "outputs": [],
311 |    "source": [
312 |     "def combine_features(row):\n",
313 |     "    return row['keywords']+\" \"+row['cast']+\" \"+row['genres']+\" \"+row['director']"
314 |    ]
315 |   },
316 |   {
317 |    "cell_type": "code",
318 |    "execution_count": 6,
319 |    "metadata": {},
320 |    "outputs": [],
321 |    "source": [
322 |     "for feature in features:\n",
323 |     "    df[feature] = df[feature].fillna('')\n",
324 |     "\n",
325 |     "df[\"combined_features\"] = df.apply(combine_features,axis=1)"
326 |    ]
327 |   },
328 |   {
329 |    "cell_type": "code",
330 |    "execution_count": 7,
331 |    "metadata": {},
332 |    "outputs": [],
333 |    "source": [
334 |     "cv = CountVectorizer() \n",
335 |     "count_matrix = cv.fit_transform(df[\"combined_features\"])"
336 |    ]
337 |   },
338 |   {
339 |    "cell_type": "code",
340 |    "execution_count": 8,
341 |    "metadata": {},
342 |    "outputs": [],
343 |    "source": [
344 |     "cosine_sim = cosine_similarity(count_matrix)"
345 |    ]
346 |   },
347 |   {
348 |    "cell_type": "code",
349 |    "execution_count": 9,
350 |    "metadata": {},
351 |    "outputs": [],
352 |    "source": [
353 |     "def get_title_from_index(index):\n",
354 |     "    return df[df.index == index][\"title\"].values[0]\n",
355 |     "def get_index_from_title(title):\n",
356 |     "    return df[df.title == title][\"index\"].values[0]"
357 |    ]
358 |   },
359 |   {
360 |    "cell_type": "code",
361 |    "execution_count": 10,
362 |    "metadata": {},
363 |    "outputs": [],
364 |    "source": [
365 |     "movie_user_likes = \"Avatar\"\n",
366 |     "movie_index = get_index_from_title(movie_user_likes)\n",
367 |     "similar_movies = list(enumerate(cosine_sim[movie_index]))"
368 |    ]
369 |   },
370 |   {
371 |    "cell_type": "code",
372 |    "execution_count": 11,
373 |    "metadata": {},
374 |    "outputs": [],
375 |    "source": [
376 |     "sorted_similar_movies = sorted(similar_movies,key=lambda x:x[1],reverse=True)[1:]"
377 |    ]
378 |   },
379 |   {
380 |    "cell_type": "code",
381 |    "execution_count": 12,
382 |    "metadata": {},
383 |    "outputs": [
384 |     {
385 |      "name": "stdout",
386 |      "output_type": "stream",
387 |      "text": [
388 |       "Top 5 similar movies to Avatar are:\n",
389 |       "\n",
390 |       "Guardians of the Galaxy\n",
391 |       "Aliens\n",
392 |       "Star Wars: Clone Wars: Volume 1\n",
393 |       "Star Trek Into Darkness\n",
394 |       "Star Trek Beyond\n",
395 |       "Alien\n"
396 |      ]
397 |     }
398 |    ],
399 |    "source": [
400 |     "i=0\n",
401 |     "print(\"Top 5 similar movies to \"+movie_user_likes+\" are:\\n\")\n",
402 |     "for element in sorted_similar_movies:\n",
403 |     "    print(get_title_from_index(element[0]))\n",
404 |     "    i=i+1\n",
405 |     "    if i>5:\n",
406 |     "        break"
407 |    ]
408 |   },
409 |   {
410 |    "cell_type": "code",
411 |    "execution_count": null,
412 |    "metadata": {},
413 |    "outputs": [],
414 |    "source": []
415 |   }
416 |  ],
417 |  "metadata": {
418 |   "kernelspec": {
419 |    "display_name": "Python 3",
420 |    "language": "python",
421 |    "name": "python3"
422 |   },
423 |   "language_info": {
424 |    "codemirror_mode": {
425 |     "name": "ipython",
426 |     "version": 3
427 |    },
428 |    "file_extension": ".py",
429 |    "mimetype": "text/x-python",
430 |    "name": "python",
431 |    "nbconvert_exporter": "python",
432 |    "pygments_lexer": "ipython3",
433 |    "version": "3.6.4"
434 |   }
435 |  },
436 |  "nbformat": 4,
437 |  "nbformat_minor": 2
438 | }
439 | 


--------------------------------------------------------------------------------
/Assignment_7.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "723eb7a5",
  7 |    "metadata": {},
  8 |    "outputs": [
  9 |     {
 10 |      "name": "stderr",
 11 |      "output_type": "stream",
 12 |      "text": [
 13 |       "[nltk_data] Downloading package punkt to\n",
 14 |       "[nltk_data]     C:\\Users\\pcoec\\AppData\\Roaming\\nltk_data...\n",
 15 |       "[nltk_data]   Package punkt is already up-to-date!\n"
 16 |      ]
 17 |     },
 18 |     {
 19 |      "data": {
 20 |       "text/plain": [
 21 |        "True"
 22 |       ]
 23 |      },
 24 |      "execution_count": 1,
 25 |      "metadata": {},
 26 |      "output_type": "execute_result"
 27 |     }
 28 |    ],
 29 |    "source": [
 30 |     "import nltk\n",
 31 |     "nltk.download('punkt')"
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "code",
 36 |    "execution_count": 4,
 37 |    "id": "a8b6b982",
 38 |    "metadata": {},
 39 |    "outputs": [
 40 |     {
 41 |      "name": "stdout",
 42 |      "output_type": "stream",
 43 |      "text": [
 44 |       "['Sachin', 'is', 'considered', 'to', 'be', 'one', 'of', 'the', 'greatest', 'cricket', 'players', '.', 'Virat', 'is', 'the', 'captain', 'of', 'the', 'Indian', 'cricket', 'team']\n",
 45 |       "['Sachin is considered to be one of the greatest cricket players.', 'Virat is the captain of the Indian cricket team']\n"
 46 |      ]
 47 |     }
 48 |    ],
 49 |    "source": [
 50 |     "from nltk import word_tokenize, sent_tokenize\n",
 51 |     "sent = \"Sachin is considered to be one of the greatest cricket players. Virat is the captain of the Indian cricket team\"\n",
 52 |     "print(word_tokenize(sent))\n",
 53 |     "print(sent_tokenize(sent))"
 54 |    ]
 55 |   },
 56 |   {
 57 |    "cell_type": "code",
 58 |    "execution_count": 5,
 59 |    "id": "308116bb",
 60 |    "metadata": {},
 61 |    "outputs": [
 62 |     {
 63 |      "name": "stdout",
 64 |      "output_type": "stream",
 65 |      "text": [
 66 |       "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
 67 |      ]
 68 |     },
 69 |     {
 70 |      "name": "stderr",
 71 |      "output_type": "stream",
 72 |      "text": [
 73 |       "[nltk_data] Downloading package stopwords to\n",
 74 |       "[nltk_data]     C:\\Users\\pcoec\\AppData\\Roaming\\nltk_data...\n",
 75 |       "[nltk_data]   Package stopwords is already up-to-date!\n"
 76 |      ]
 77 |     }
 78 |    ],
 79 |    "source": [
 80 |     "from nltk.corpus import stopwords\n",
 81 |     "import nltk\n",
 82 |     "nltk.download('stopwords')\n",
 83 |     "stop_words = stopwords.words('english')\n",
 84 |     "print(stop_words)"
 85 |    ]
 86 |   },
 87 |   {
 88 |    "cell_type": "code",
 89 |    "execution_count": 13,
 90 |    "id": "f8411fe8",
 91 |    "metadata": {},
 92 |    "outputs": [
 93 |     {
 94 |      "name": "stdout",
 95 |      "output_type": "stream",
 96 |      "text": [
 97 |       "This is the unclean version :  ['Sachin', 'is', 'considered', 'to', 'be', 'one', 'of', 'the', 'greatest', 'cricket', 'players', '.', 'Virat', 'is', 'the', 'captain', 'of', 'the', 'Indian', 'cricket', 'team']\n",
 98 |       "This is the cleaned version :  ['Sachin', 'considered', 'one', 'greatest', 'cricket', 'players', '.', 'Virat', 'captain', 'Indian', 'cricket', 'team']\n"
 99 |      ]
100 |     }
101 |    ],
102 |    "source": [
103 |     "token = word_tokenize(sent)\n",
104 |     "cleaned_token = []\n",
105 |     "for word in token:\n",
106 |     " if word not in stop_words:\n",
107 |     "    cleaned_token.append(word)\n",
108 |     "\n",
109 |     "print(\"This is the unclean version : \",token)\n",
110 |     "print(\"This is the cleaned version : \",cleaned_token)"
111 |    ]
112 |   },
113 |   {
114 |    "cell_type": "code",
115 |    "execution_count": 15,
116 |    "id": "57060168",
117 |    "metadata": {},
118 |    "outputs": [],
119 |    "source": [
120 |     "words = [cleaned_token.lower() for cleaned_token in cleaned_token if cleaned_token.isalpha()]"
121 |    ]
122 |   },
123 |   {
124 |    "cell_type": "code",
125 |    "execution_count": 16,
126 |    "id": "4a59215e",
127 |    "metadata": {},
128 |    "outputs": [
129 |     {
130 |      "name": "stdout",
131 |      "output_type": "stream",
132 |      "text": [
133 |       "['sachin', 'considered', 'one', 'greatest', 'cricket', 'players', 'virat', 'captain', 'indian', 'cricket', 'team']\n"
134 |      ]
135 |     }
136 |    ],
137 |    "source": [
138 |     "print(words)"
139 |    ]
140 |   },
141 |   {
142 |    "cell_type": "code",
143 |    "execution_count": 17,
144 |    "id": "d682e8a3",
145 |    "metadata": {},
146 |    "outputs": [
147 |     {
148 |      "name": "stdout",
149 |      "output_type": "stream",
150 |      "text": [
151 |       "['sachin', 'consid', 'one', 'greatest', 'cricket', 'player', 'virat', 'captain', 'indian', 'cricket', 'team']\n"
152 |      ]
153 |     }
154 |    ],
155 |    "source": [
156 |     "from nltk.stem import PorterStemmer\n",
157 |     "stemmer = PorterStemmer()\n",
158 |     "port_stemmer_output = [stemmer.stem(words) for words in words]\n",
159 |     "print(port_stemmer_output)\n"
160 |    ]
161 |   },
162 |   {
163 |    "cell_type": "code",
164 |    "execution_count": 18,
165 |    "id": "8c31f238",
166 |    "metadata": {},
167 |    "outputs": [
168 |     {
169 |      "name": "stderr",
170 |      "output_type": "stream",
171 |      "text": [
172 |       "[nltk_data] Downloading package wordnet to\n",
173 |       "[nltk_data]     C:\\Users\\pcoec\\AppData\\Roaming\\nltk_data...\n",
174 |       "[nltk_data]   Package wordnet is already up-to-date!\n"
175 |      ]
176 |     },
177 |     {
178 |      "name": "stdout",
179 |      "output_type": "stream",
180 |      "text": [
181 |       "['sachin', 'considered', 'one', 'greatest', 'cricket', 'player', 'virat', 'captain', 'indian', 'cricket', 'team']\n"
182 |      ]
183 |     }
184 |    ],
185 |    "source": [
186 |     "from nltk.stem import WordNetLemmatizer\n",
187 |     "nltk.download('wordnet')\n",
188 |     "lemmatizer = WordNetLemmatizer()\n",
189 |     "lemmatizer_output = [lemmatizer.lemmatize(words) for words in words]\n",
190 |     "print(lemmatizer_output)\n"
191 |    ]
192 |   },
193 |   {
194 |    "cell_type": "code",
195 |    "execution_count": 20,
196 |    "id": "f6bede85",
197 |    "metadata": {},
198 |    "outputs": [
199 |     {
200 |      "name": "stdout",
201 |      "output_type": "stream",
202 |      "text": [
203 |       "[('Sachin', 'NNP'), ('considered', 'VBD'), ('one', 'CD'), ('greatest', 'JJS'), ('cricket', 'NN'), ('players', 'NNS'), ('.', '.'), ('Virat', 'NNP'), ('captain', 'NN'), ('Indian', 'JJ'), ('cricket', 'NN'), ('team', 'NN')]\n"
204 |      ]
205 |     },
206 |     {
207 |      "name": "stderr",
208 |      "output_type": "stream",
209 |      "text": [
210 |       "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
211 |       "[nltk_data]     C:\\Users\\pcoec\\AppData\\Roaming\\nltk_data...\n",
212 |       "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
213 |       "[nltk_data]       date!\n"
214 |      ]
215 |     }
216 |    ],
217 |    "source": [
218 |     "from nltk import pos_tag\n",
219 |     "import nltk\n",
220 |     "nltk.download('averaged_perceptron_tagger')\n",
221 |     "token = word_tokenize(sent)\n",
222 |     "cleaned_token = []\n",
223 |     "for word in token:\n",
224 |     " if word not in stop_words:\n",
225 |     "    cleaned_token.append(word)\n",
226 |     "tagged = pos_tag(cleaned_token)\n",
227 |     "print(tagged)"
228 |    ]
229 |   },
230 |   {
231 |    "cell_type": "code",
232 |    "execution_count": 21,
233 |    "id": "2a95cc8a",
234 |    "metadata": {},
235 |    "outputs": [],
236 |    "source": [
237 |     "from sklearn.feature_extraction.text import TfidfVectorizer\n",
238 |     "from sklearn.metrics.pairwise import cosine_similarity\n",
239 |     "import pandas as pd"
240 |    ]
241 |   },
242 |   {
243 |    "cell_type": "code",
244 |    "execution_count": 22,
245 |    "id": "a65a6fe4",
246 |    "metadata": {},
247 |    "outputs": [],
248 |    "source": [
249 |     "docs = [ \"Sachin is considered to be one of the greatest cricket players\",\n",
250 |     " \"Federer is considered one of the greatest tennis players\",\n",
251 |     " \"Nadal is considered one of the greatest tennis players\",\n",
252 |     " \"Virat is the captain of the Indian cricket team\"]"
253 |    ]
254 |   },
255 |   {
256 |    "cell_type": "code",
257 |    "execution_count": 24,
258 |    "id": "badd7e5a",
259 |    "metadata": {},
260 |    "outputs": [
261 |     {
262 |      "name": "stdout",
263 |      "output_type": "stream",
264 |      "text": [
265 |       "{'sachin': 12, 'is': 7, 'considered': 2, 'to': 16, 'be': 0, 'one': 10, 'of': 9, 'the': 15, 'greatest': 5, 'cricket': 3, 'players': 11, 'federer': 4, 'tennis': 14, 'nadal': 8, 'virat': 17, 'captain': 1, 'indian': 6, 'team': 13}\n"
266 |      ]
267 |     }
268 |    ],
269 |    "source": [
270 |     "vectorizer = TfidfVectorizer(analyzer = \"word\", norm = None , use_idf = True , smooth_idf=True)\n",
271 |     "Mat = vectorizer.fit(docs)\n",
272 |     "print(Mat.vocabulary_)"
273 |    ]
274 |   },
275 |   {
276 |    "cell_type": "code",
277 |    "execution_count": 25,
278 |    "id": "36c09e91",
279 |    "metadata": {},
280 |    "outputs": [],
281 |    "source": [
282 |     "tfidfMat = vectorizer.fit_transform(docs)"
283 |    ]
284 |   },
285 |   {
286 |    "cell_type": "code",
287 |    "execution_count": 26,
288 |    "id": "16a49f3a",
289 |    "metadata": {},
290 |    "outputs": [
291 |     {
292 |      "name": "stdout",
293 |      "output_type": "stream",
294 |      "text": [
295 |       "  (0, 11)\t1.2231435513142097\n",
296 |       "  (0, 3)\t1.5108256237659907\n",
297 |       "  (0, 5)\t1.2231435513142097\n",
298 |       "  (0, 15)\t1.0\n",
299 |       "  (0, 9)\t1.0\n",
300 |       "  (0, 10)\t1.2231435513142097\n",
301 |       "  (0, 0)\t1.916290731874155\n",
302 |       "  (0, 16)\t1.916290731874155\n",
303 |       "  (0, 2)\t1.2231435513142097\n",
304 |       "  (0, 7)\t1.0\n",
305 |       "  (0, 12)\t1.916290731874155\n",
306 |       "  (1, 14)\t1.5108256237659907\n",
307 |       "  (1, 4)\t1.916290731874155\n",
308 |       "  (1, 11)\t1.2231435513142097\n",
309 |       "  (1, 5)\t1.2231435513142097\n",
310 |       "  (1, 15)\t1.0\n",
311 |       "  (1, 9)\t1.0\n",
312 |       "  (1, 10)\t1.2231435513142097\n",
313 |       "  (1, 2)\t1.2231435513142097\n",
314 |       "  (1, 7)\t1.0\n",
315 |       "  (2, 8)\t1.916290731874155\n",
316 |       "  (2, 14)\t1.5108256237659907\n",
317 |       "  (2, 11)\t1.2231435513142097\n",
318 |       "  (2, 5)\t1.2231435513142097\n",
319 |       "  (2, 15)\t1.0\n",
320 |       "  (2, 9)\t1.0\n",
321 |       "  (2, 10)\t1.2231435513142097\n",
322 |       "  (2, 2)\t1.2231435513142097\n",
323 |       "  (2, 7)\t1.0\n",
324 |       "  (3, 13)\t1.916290731874155\n",
325 |       "  (3, 6)\t1.916290731874155\n",
326 |       "  (3, 1)\t1.916290731874155\n",
327 |       "  (3, 17)\t1.916290731874155\n",
328 |       "  (3, 3)\t1.5108256237659907\n",
329 |       "  (3, 15)\t2.0\n",
330 |       "  (3, 9)\t1.0\n",
331 |       "  (3, 7)\t1.0\n"
332 |      ]
333 |     }
334 |    ],
335 |    "source": [
336 |     "print(tfidfMat)\n"
337 |    ]
338 |   },
339 |   {
340 |    "cell_type": "code",
341 |    "execution_count": 27,
342 |    "id": "93c5dcc0",
343 |    "metadata": {},
344 |    "outputs": [
345 |     {
346 |      "name": "stdout",
347 |      "output_type": "stream",
348 |      "text": [
349 |       "['be' 'captain' 'considered' 'cricket' 'federer' 'greatest' 'indian' 'is'\n",
350 |       " 'nadal' 'of' 'one' 'players' 'sachin' 'team' 'tennis' 'the' 'to' 'virat']\n"
351 |      ]
352 |     }
353 |    ],
354 |    "source": [
355 |     "features_names = vectorizer.get_feature_names_out()\n",
356 |     "print(features_names)\n"
357 |    ]
358 |   },
359 |   {
360 |    "cell_type": "code",
361 |    "execution_count": 28,
362 |    "id": "514ea886",
363 |    "metadata": {},
364 |    "outputs": [],
365 |    "source": [
366 |     "dense = tfidfMat.todense()\n",
367 |     "denselist = dense.tolist()\n",
368 |     "df = pd.DataFrame(denselist , columns = features_names)"
369 |    ]
370 |   },
371 |   {
372 |    "cell_type": "code",
373 |    "execution_count": 29,
374 |    "id": "160bf2f9",
375 |    "metadata": {},
376 |    "outputs": [
377 |     {
378 |      "data": {
379 |       "text/html": [
380 |        "<div>\n",
381 |        "<style scoped>\n",
382 |        "    .dataframe tbody tr th:only-of-type {\n",
383 |        "        vertical-align: middle;\n",
384 |        "    }\n",
385 |        "\n",
386 |        "    .dataframe tbody tr th {\n",
387 |        "        vertical-align: top;\n",
388 |        "    }\n",
389 |        "\n",
390 |        "    .dataframe thead th {\n",
391 |        "        text-align: right;\n",
392 |        "    }\n",
393 |        "</style>\n",
394 |        "<table border=\"1\" class=\"dataframe\">\n",
395 |        "  <thead>\n",
396 |        "    <tr style=\"text-align: right;\">\n",
397 |        "      <th></th>\n",
398 |        "      <th>be</th>\n",
399 |        "      <th>captain</th>\n",
400 |        "      <th>considered</th>\n",
401 |        "      <th>cricket</th>\n",
402 |        "      <th>federer</th>\n",
403 |        "      <th>greatest</th>\n",
404 |        "      <th>indian</th>\n",
405 |        "      <th>is</th>\n",
406 |        "      <th>nadal</th>\n",
407 |        "      <th>of</th>\n",
408 |        "      <th>one</th>\n",
409 |        "      <th>players</th>\n",
410 |        "      <th>sachin</th>\n",
411 |        "      <th>team</th>\n",
412 |        "      <th>tennis</th>\n",
413 |        "      <th>the</th>\n",
414 |        "      <th>to</th>\n",
415 |        "      <th>virat</th>\n",
416 |        "    </tr>\n",
417 |        "  </thead>\n",
418 |        "  <tbody>\n",
419 |        "    <tr>\n",
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432 |        "      <td>1.223144</td>\n",
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439 |        "    </tr>\n",
440 |        "    <tr>\n",
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443 |        "      <td>0.000000</td>\n",
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447 |        "      <td>1.223144</td>\n",
448 |        "      <td>0.000000</td>\n",
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460 |        "    </tr>\n",
461 |        "    <tr>\n",
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505 |        "</div>"
506 |       ],
507 |       "text/plain": [
508 |        "         be   captain  considered   cricket   federer  greatest    indian  \\\n",
509 |        "0  1.916291  0.000000    1.223144  1.510826  0.000000  1.223144  0.000000   \n",
510 |        "1  0.000000  0.000000    1.223144  0.000000  1.916291  1.223144  0.000000   \n",
511 |        "2  0.000000  0.000000    1.223144  0.000000  0.000000  1.223144  0.000000   \n",
512 |        "3  0.000000  1.916291    0.000000  1.510826  0.000000  0.000000  1.916291   \n",
513 |        "\n",
514 |        "    is     nadal   of       one   players    sachin      team    tennis  the  \\\n",
515 |        "0  1.0  0.000000  1.0  1.223144  1.223144  1.916291  0.000000  0.000000  1.0   \n",
516 |        "1  1.0  0.000000  1.0  1.223144  1.223144  0.000000  0.000000  1.510826  1.0   \n",
517 |        "2  1.0  1.916291  1.0  1.223144  1.223144  0.000000  0.000000  1.510826  1.0   \n",
518 |        "3  1.0  0.000000  1.0  0.000000  0.000000  0.000000  1.916291  0.000000  2.0   \n",
519 |        "\n",
520 |        "         to     virat  \n",
521 |        "0  1.916291  0.000000  \n",
522 |        "1  0.000000  0.000000  \n",
523 |        "2  0.000000  0.000000  \n",
524 |        "3  0.000000  1.916291  "
525 |       ]
526 |      },
527 |      "execution_count": 29,
528 |      "metadata": {},
529 |      "output_type": "execute_result"
530 |     }
531 |    ],
532 |    "source": [
533 |     "df"
534 |    ]
535 |   },
536 |   {
537 |    "cell_type": "code",
538 |    "execution_count": 30,
539 |    "id": "37520ccc",
540 |    "metadata": {},
541 |    "outputs": [
542 |     {
543 |      "ename": "AttributeError",
544 |      "evalue": "'TfidfVectorizer' object has no attribute 'get_feature_names'",
545 |      "output_type": "error",
546 |      "traceback": [
547 |       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
548 |       "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
549 |       "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_16340\\2669239957.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfeatures_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvectorizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_feature_names\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
550 |       "\u001b[1;31mAttributeError\u001b[0m: 'TfidfVectorizer' object has no attribute 'get_feature_names'"
551 |      ]
552 |     }
553 |    ],
554 |    "source": [
555 |     "features_names = sorted(vectorizer.get_feature_names())"
556 |    ]
557 |   },
558 |   {
559 |    "cell_type": "code",
560 |    "execution_count": 32,
561 |    "id": "a02ac24f",
562 |    "metadata": {},
563 |    "outputs": [
564 |     {
565 |      "name": "stdout",
566 |      "output_type": "stream",
567 |      "text": [
568 |       "             be   captain  considered   cricket   federer  greatest    indian  \\\n",
569 |       "Doc 1  1.916291  0.000000    1.223144  1.510826  0.000000  1.223144  0.000000   \n",
570 |       "Doc 2  0.000000  0.000000    1.223144  0.000000  1.916291  1.223144  0.000000   \n",
571 |       "Doc 3  0.000000  0.000000    1.223144  0.000000  0.000000  1.223144  0.000000   \n",
572 |       "Doc 4  0.000000  1.916291    0.000000  1.510826  0.000000  0.000000  1.916291   \n",
573 |       "\n",
574 |       "        is     nadal   of       one   players    sachin      team    tennis  \\\n",
575 |       "Doc 1  1.0  0.000000  1.0  1.223144  1.223144  1.916291  0.000000  0.000000   \n",
576 |       "Doc 2  1.0  0.000000  1.0  1.223144  1.223144  0.000000  0.000000  1.510826   \n",
577 |       "Doc 3  1.0  1.916291  1.0  1.223144  1.223144  0.000000  0.000000  1.510826   \n",
578 |       "Doc 4  1.0  0.000000  1.0  0.000000  0.000000  0.000000  1.916291  0.000000   \n",
579 |       "\n",
580 |       "       the        to     virat  \n",
581 |       "Doc 1  1.0  1.916291  0.000000  \n",
582 |       "Doc 2  1.0  0.000000  0.000000  \n",
583 |       "Doc 3  1.0  0.000000  0.000000  \n",
584 |       "Doc 4  2.0  0.000000  1.916291  \n"
585 |      ]
586 |     }
587 |    ],
588 |    "source": [
589 |     "docList = ['Doc 1','Doc 2','Doc 3','Doc 4']\n",
590 |     "skDocsIfIdfdf = pd.DataFrame(tfidfMat.todense(),index = sorted(docList), columns=features_names)\n",
591 |     "print(skDocsIfIdfdf)"
592 |    ]
593 |   },
594 |   {
595 |    "cell_type": "code",
596 |    "execution_count": 33,
597 |    "id": "a1ed9a3a",
598 |    "metadata": {},
599 |    "outputs": [],
600 |    "source": [
601 |     "csim = cosine_similarity(tfidfMat,tfidfMat)\n"
602 |    ]
603 |   },
604 |   {
605 |    "cell_type": "code",
606 |    "execution_count": 34,
607 |    "id": "9555f330",
608 |    "metadata": {},
609 |    "outputs": [],
610 |    "source": [
611 |     "csimDf = pd.DataFrame(csim,index=sorted(docList),columns=sorted(docList))\n"
612 |    ]
613 |   },
614 |   {
615 |    "cell_type": "code",
616 |    "execution_count": 35,
617 |    "id": "e8ea2806",
618 |    "metadata": {},
619 |    "outputs": [
620 |     {
621 |      "name": "stdout",
622 |      "output_type": "stream",
623 |      "text": [
624 |       "          Doc 1     Doc 2     Doc 3     Doc 4\n",
625 |       "Doc 1  1.000000  0.492416  0.492416  0.277687\n",
626 |       "Doc 2  0.492416  1.000000  0.754190  0.215926\n",
627 |       "Doc 3  0.492416  0.754190  1.000000  0.215926\n",
628 |       "Doc 4  0.277687  0.215926  0.215926  1.000000\n"
629 |      ]
630 |     }
631 |    ],
632 |    "source": [
633 |     "print(csimDf)\n"
634 |    ]
635 |   }
636 |  ],
637 |  "metadata": {
638 |   "kernelspec": {
639 |    "display_name": "Python 3 (ipykernel)",
640 |    "language": "python",
641 |    "name": "python3"
642 |   },
643 |   "language_info": {
644 |    "codemirror_mode": {
645 |     "name": "ipython",
646 |     "version": 3
647 |    },
648 |    "file_extension": ".py",
649 |    "mimetype": "text/x-python",
650 |    "name": "python",
651 |    "nbconvert_exporter": "python",
652 |    "pygments_lexer": "ipython3",
653 |    "version": "3.9.13"
654 |   }
655 |  },
656 |  "nbformat": 4,
657 |  "nbformat_minor": 5
658 | }
659 | 


--------------------------------------------------------------------------------
/BostonHousing.csv:
--------------------------------------------------------------------------------
  1 | "crim","zn","indus","chas","nox","rm","age","dis","rad","tax","ptratio","b","lstat","medv"
  2 | 0.00632,18,2.31,"0",0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24
  3 | 0.02731,0,7.07,"0",0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6
  4 | 0.02729,0,7.07,"0",0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7
  5 | 0.03237,0,2.18,"0",0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94,33.4
  6 | 0.06905,0,2.18,"0",0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33,36.2
  7 | 0.02985,0,2.18,"0",0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7
  8 | 0.08829,12.5,7.87,"0",0.524,6.012,66.6,5.5605,5,311,15.2,395.6,12.43,22.9
  9 | 0.14455,12.5,7.87,"0",0.524,6.172,96.1,5.9505,5,311,15.2,396.9,19.15,27.1
 10 | 0.21124,12.5,7.87,"0",0.524,5.631,100,6.0821,5,311,15.2,386.63,29.93,16.5
 11 | 0.17004,12.5,7.87,"0",0.524,6.004,85.9,6.5921,5,311,15.2,386.71,17.1,18.9
 12 | 0.22489,12.5,7.87,"0",0.524,6.377,94.3,6.3467,5,311,15.2,392.52,20.45,15
 13 | 0.11747,12.5,7.87,"0",0.524,6.009,82.9,6.2267,5,311,15.2,396.9,13.27,18.9
 14 | 0.09378,12.5,7.87,"0",0.524,5.889,39,5.4509,5,311,15.2,390.5,15.71,21.7
 15 | 0.62976,0,8.14,"0",0.538,5.949,61.8,4.7075,4,307,21,396.9,8.26,20.4
 16 | 0.63796,0,8.14,"0",0.538,6.096,84.5,4.4619,4,307,21,380.02,10.26,18.2
 17 | 0.62739,0,8.14,"0",0.538,5.834,56.5,4.4986,4,307,21,395.62,8.47,19.9
 18 | 1.05393,0,8.14,"0",0.538,5.935,29.3,4.4986,4,307,21,386.85,6.58,23.1
 19 | 0.7842,0,8.14,"0",0.538,5.99,81.7,4.2579,4,307,21,386.75,14.67,17.5
 20 | 0.80271,0,8.14,"0",0.538,5.456,36.6,3.7965,4,307,21,288.99,11.69,20.2
 21 | 0.7258,0,8.14,"0",0.538,5.727,69.5,3.7965,4,307,21,390.95,11.28,18.2
 22 | 1.25179,0,8.14,"0",0.538,5.57,98.1,3.7979,4,307,21,376.57,21.02,13.6
 23 | 0.85204,0,8.14,"0",0.538,5.965,89.2,4.0123,4,307,21,392.53,13.83,19.6
 24 | 1.23247,0,8.14,"0",0.538,6.142,91.7,3.9769,4,307,21,396.9,18.72,15.2
 25 | 0.98843,0,8.14,"0",0.538,5.813,100,4.0952,4,307,21,394.54,19.88,14.5
 26 | 0.75026,0,8.14,"0",0.538,5.924,94.1,4.3996,4,307,21,394.33,16.3,15.6
 27 | 0.84054,0,8.14,"0",0.538,5.599,85.7,4.4546,4,307,21,303.42,16.51,13.9
 28 | 0.67191,0,8.14,"0",0.538,5.813,90.3,4.682,4,307,21,376.88,14.81,16.6
 29 | 0.95577,0,8.14,"0",0.538,6.047,88.8,4.4534,4,307,21,306.38,17.28,14.8
 30 | 0.77299,0,8.14,"0",0.538,6.495,94.4,4.4547,4,307,21,387.94,12.8,18.4
 31 | 1.00245,0,8.14,"0",0.538,6.674,87.3,4.239,4,307,21,380.23,11.98,21
 32 | 1.13081,0,8.14,"0",0.538,5.713,94.1,4.233,4,307,21,360.17,22.6,12.7
 33 | 1.35472,0,8.14,"0",0.538,6.072,100,4.175,4,307,21,376.73,13.04,14.5
 34 | 1.38799,0,8.14,"0",0.538,5.95,82,3.99,4,307,21,232.6,27.71,13.2
 35 | 1.15172,0,8.14,"0",0.538,5.701,95,3.7872,4,307,21,358.77,18.35,13.1
 36 | 1.61282,0,8.14,"0",0.538,6.096,96.9,3.7598,4,307,21,248.31,20.34,13.5
 37 | 0.06417,0,5.96,"0",0.499,5.933,68.2,3.3603,5,279,19.2,396.9,9.68,18.9
 38 | 0.09744,0,5.96,"0",0.499,5.841,61.4,3.3779,5,279,19.2,377.56,11.41,20
 39 | 0.08014,0,5.96,"0",0.499,5.85,41.5,3.9342,5,279,19.2,396.9,8.77,21
 40 | 0.17505,0,5.96,"0",0.499,5.966,30.2,3.8473,5,279,19.2,393.43,10.13,24.7
 41 | 0.02763,75,2.95,"0",0.428,6.595,21.8,5.4011,3,252,18.3,395.63,4.32,30.8
 42 | 0.03359,75,2.95,"0",0.428,7.024,15.8,5.4011,3,252,18.3,395.62,1.98,34.9
 43 | 0.12744,0,6.91,"0",0.448,6.77,2.9,5.7209,3,233,17.9,385.41,4.84,26.6
 44 | 0.1415,0,6.91,"0",0.448,6.169,6.6,5.7209,3,233,17.9,383.37,5.81,25.3
 45 | 0.15936,0,6.91,"0",0.448,6.211,6.5,5.7209,3,233,17.9,394.46,7.44,24.7
 46 | 0.12269,0,6.91,"0",0.448,6.069,40,5.7209,3,233,17.9,389.39,9.55,21.2
 47 | 0.17142,0,6.91,"0",0.448,5.682,33.8,5.1004,3,233,17.9,396.9,10.21,19.3
 48 | 0.18836,0,6.91,"0",0.448,5.786,33.3,5.1004,3,233,17.9,396.9,14.15,20
 49 | 0.22927,0,6.91,"0",0.448,6.03,85.5,5.6894,3,233,17.9,392.74,18.8,16.6
 50 | 0.25387,0,6.91,"0",0.448,5.399,95.3,5.87,3,233,17.9,396.9,30.81,14.4
 51 | 0.21977,0,6.91,"0",0.448,5.602,62,6.0877,3,233,17.9,396.9,16.2,19.4
 52 | 0.08873,21,5.64,"0",0.439,5.963,45.7,6.8147,4,243,16.8,395.56,13.45,19.7
 53 | 0.04337,21,5.64,"0",0.439,6.115,63,6.8147,4,243,16.8,393.97,9.43,20.5
 54 | 0.0536,21,5.64,"0",0.439,6.511,21.1,6.8147,4,243,16.8,396.9,5.28,25
 55 | 0.04981,21,5.64,"0",0.439,5.998,21.4,6.8147,4,243,16.8,396.9,8.43,23.4
 56 | 0.0136,75,4,"0",0.41,5.888,47.6,7.3197,3,469,21.1,396.9,14.8,18.9
 57 | 0.01311,90,1.22,"0",0.403,7.249,21.9,8.6966,5,226,17.9,395.93,4.81,35.4
 58 | 0.02055,85,0.74,"0",0.41,6.383,35.7,9.1876,2,313,17.3,396.9,5.77,24.7
 59 | 0.01432,100,1.32,"0",0.411,6.816,40.5,8.3248,5,256,15.1,392.9,3.95,31.6
 60 | 0.15445,25,5.13,"0",0.453,6.145,29.2,7.8148,8,284,19.7,390.68,6.86,23.3
 61 | 0.10328,25,5.13,"0",0.453,5.927,47.2,6.932,8,284,19.7,396.9,9.22,19.6
 62 | 0.14932,25,5.13,"0",0.453,5.741,66.2,7.2254,8,284,19.7,395.11,13.15,18.7
 63 | 0.17171,25,5.13,"0",0.453,5.966,93.4,6.8185,8,284,19.7,378.08,14.44,16
 64 | 0.11027,25,5.13,"0",0.453,6.456,67.8,7.2255,8,284,19.7,396.9,6.73,22.2
 65 | 0.1265,25,5.13,"0",0.453,6.762,43.4,7.9809,8,284,19.7,395.58,9.5,25
 66 | 0.01951,17.5,1.38,"0",0.4161,7.104,59.5,9.2229,3,216,18.6,393.24,8.05,33
 67 | 0.03584,80,3.37,"0",0.398,6.29,17.8,6.6115,4,337,16.1,396.9,4.67,23.5
 68 | 0.04379,80,3.37,"0",0.398,5.787,31.1,6.6115,4,337,16.1,396.9,10.24,19.4
 69 | 0.05789,12.5,6.07,"0",0.409,5.878,21.4,6.498,4,345,18.9,396.21,8.1,22
 70 | 0.13554,12.5,6.07,"0",0.409,5.594,36.8,6.498,4,345,18.9,396.9,13.09,17.4
 71 | 0.12816,12.5,6.07,"0",0.409,5.885,33,6.498,4,345,18.9,396.9,8.79,20.9
 72 | 0.08826,0,10.81,"0",0.413,6.417,6.6,5.2873,4,305,19.2,383.73,6.72,24.2
 73 | 0.15876,0,10.81,"0",0.413,5.961,17.5,5.2873,4,305,19.2,376.94,9.88,21.7
 74 | 0.09164,0,10.81,"0",0.413,6.065,7.8,5.2873,4,305,19.2,390.91,5.52,22.8
 75 | 0.19539,0,10.81,"0",0.413,6.245,6.2,5.2873,4,305,19.2,377.17,7.54,23.4
 76 | 0.07896,0,12.83,"0",0.437,6.273,6,4.2515,5,398,18.7,394.92,6.78,24.1
 77 | 0.09512,0,12.83,"0",0.437,6.286,45,4.5026,5,398,18.7,383.23,8.94,21.4
 78 | 0.10153,0,12.83,"0",0.437,6.279,74.5,4.0522,5,398,18.7,373.66,11.97,20
 79 | 0.08707,0,12.83,"0",0.437,6.14,45.8,4.0905,5,398,18.7,386.96,10.27,20.8
 80 | 0.05646,0,12.83,"0",0.437,6.232,53.7,5.0141,5,398,18.7,386.4,12.34,21.2
 81 | 0.08387,0,12.83,"0",0.437,5.874,36.6,4.5026,5,398,18.7,396.06,9.1,20.3
 82 | 0.04113,25,4.86,"0",0.426,6.727,33.5,5.4007,4,281,19,396.9,5.29,28
 83 | 0.04462,25,4.86,"0",0.426,6.619,70.4,5.4007,4,281,19,395.63,7.22,23.9
 84 | 0.03659,25,4.86,"0",0.426,6.302,32.2,5.4007,4,281,19,396.9,6.72,24.8
 85 | 0.03551,25,4.86,"0",0.426,6.167,46.7,5.4007,4,281,19,390.64,7.51,22.9
 86 | 0.05059,0,4.49,"0",0.449,6.389,48,4.7794,3,247,18.5,396.9,9.62,23.9
 87 | 0.05735,0,4.49,"0",0.449,6.63,56.1,4.4377,3,247,18.5,392.3,6.53,26.6
 88 | 0.05188,0,4.49,"0",0.449,6.015,45.1,4.4272,3,247,18.5,395.99,12.86,22.5
 89 | 0.07151,0,4.49,"0",0.449,6.121,56.8,3.7476,3,247,18.5,395.15,8.44,22.2
 90 | 0.0566,0,3.41,"0",0.489,7.007,86.3,3.4217,2,270,17.8,396.9,5.5,23.6
 91 | 0.05302,0,3.41,"0",0.489,7.079,63.1,3.4145,2,270,17.8,396.06,5.7,28.7
 92 | 0.04684,0,3.41,"0",0.489,6.417,66.1,3.0923,2,270,17.8,392.18,8.81,22.6
 93 | 0.03932,0,3.41,"0",0.489,6.405,73.9,3.0921,2,270,17.8,393.55,8.2,22
 94 | 0.04203,28,15.04,"0",0.464,6.442,53.6,3.6659,4,270,18.2,395.01,8.16,22.9
 95 | 0.02875,28,15.04,"0",0.464,6.211,28.9,3.6659,4,270,18.2,396.33,6.21,25
 96 | 0.04294,28,15.04,"0",0.464,6.249,77.3,3.615,4,270,18.2,396.9,10.59,20.6
 97 | 0.12204,0,2.89,"0",0.445,6.625,57.8,3.4952,2,276,18,357.98,6.65,28.4
 98 | 0.11504,0,2.89,"0",0.445,6.163,69.6,3.4952,2,276,18,391.83,11.34,21.4
 99 | 0.12083,0,2.89,"0",0.445,8.069,76,3.4952,2,276,18,396.9,4.21,38.7
100 | 0.08187,0,2.89,"0",0.445,7.82,36.9,3.4952,2,276,18,393.53,3.57,43.8
101 | 0.0686,0,2.89,"0",0.445,7.416,62.5,3.4952,2,276,18,396.9,6.19,33.2
102 | 0.14866,0,8.56,"0",0.52,6.727,79.9,2.7778,5,384,20.9,394.76,9.42,27.5
103 | 0.11432,0,8.56,"0",0.52,6.781,71.3,2.8561,5,384,20.9,395.58,7.67,26.5
104 | 0.22876,0,8.56,"0",0.52,6.405,85.4,2.7147,5,384,20.9,70.8,10.63,18.6
105 | 0.21161,0,8.56,"0",0.52,6.137,87.4,2.7147,5,384,20.9,394.47,13.44,19.3
106 | 0.1396,0,8.56,"0",0.52,6.167,90,2.421,5,384,20.9,392.69,12.33,20.1
107 | 0.13262,0,8.56,"0",0.52,5.851,96.7,2.1069,5,384,20.9,394.05,16.47,19.5
108 | 0.1712,0,8.56,"0",0.52,5.836,91.9,2.211,5,384,20.9,395.67,18.66,19.5
109 | 0.13117,0,8.56,"0",0.52,6.127,85.2,2.1224,5,384,20.9,387.69,14.09,20.4
110 | 0.12802,0,8.56,"0",0.52,6.474,97.1,2.4329,5,384,20.9,395.24,12.27,19.8
111 | 0.26363,0,8.56,"0",0.52,6.229,91.2,2.5451,5,384,20.9,391.23,15.55,19.4
112 | 0.10793,0,8.56,"0",0.52,6.195,54.4,2.7778,5,384,20.9,393.49,13,21.7
113 | 0.10084,0,10.01,"0",0.547,6.715,81.6,2.6775,6,432,17.8,395.59,10.16,22.8
114 | 0.12329,0,10.01,"0",0.547,5.913,92.9,2.3534,6,432,17.8,394.95,16.21,18.8
115 | 0.22212,0,10.01,"0",0.547,6.092,95.4,2.548,6,432,17.8,396.9,17.09,18.7
116 | 0.14231,0,10.01,"0",0.547,6.254,84.2,2.2565,6,432,17.8,388.74,10.45,18.5
117 | 0.17134,0,10.01,"0",0.547,5.928,88.2,2.4631,6,432,17.8,344.91,15.76,18.3
118 | 0.13158,0,10.01,"0",0.547,6.176,72.5,2.7301,6,432,17.8,393.3,12.04,21.2
119 | 0.15098,0,10.01,"0",0.547,6.021,82.6,2.7474,6,432,17.8,394.51,10.3,19.2
120 | 0.13058,0,10.01,"0",0.547,5.872,73.1,2.4775,6,432,17.8,338.63,15.37,20.4
121 | 0.14476,0,10.01,"0",0.547,5.731,65.2,2.7592,6,432,17.8,391.5,13.61,19.3
122 | 0.06899,0,25.65,"0",0.581,5.87,69.7,2.2577,2,188,19.1,389.15,14.37,22
123 | 0.07165,0,25.65,"0",0.581,6.004,84.1,2.1974,2,188,19.1,377.67,14.27,20.3
124 | 0.09299,0,25.65,"0",0.581,5.961,92.9,2.0869,2,188,19.1,378.09,17.93,20.5
125 | 0.15038,0,25.65,"0",0.581,5.856,97,1.9444,2,188,19.1,370.31,25.41,17.3
126 | 0.09849,0,25.65,"0",0.581,5.879,95.8,2.0063,2,188,19.1,379.38,17.58,18.8
127 | 0.16902,0,25.65,"0",0.581,5.986,88.4,1.9929,2,188,19.1,385.02,14.81,21.4
128 | 0.38735,0,25.65,"0",0.581,5.613,95.6,1.7572,2,188,19.1,359.29,27.26,15.7
129 | 0.25915,0,21.89,"0",0.624,5.693,96,1.7883,4,437,21.2,392.11,17.19,16.2
130 | 0.32543,0,21.89,"0",0.624,6.431,98.8,1.8125,4,437,21.2,396.9,15.39,18
131 | 0.88125,0,21.89,"0",0.624,5.637,94.7,1.9799,4,437,21.2,396.9,18.34,14.3
132 | 0.34006,0,21.89,"0",0.624,6.458,98.9,2.1185,4,437,21.2,395.04,12.6,19.2
133 | 1.19294,0,21.89,"0",0.624,6.326,97.7,2.271,4,437,21.2,396.9,12.26,19.6
134 | 0.59005,0,21.89,"0",0.624,6.372,97.9,2.3274,4,437,21.2,385.76,11.12,23
135 | 0.32982,0,21.89,"0",0.624,5.822,95.4,2.4699,4,437,21.2,388.69,15.03,18.4
136 | 0.97617,0,21.89,"0",0.624,5.757,98.4,2.346,4,437,21.2,262.76,17.31,15.6
137 | 0.55778,0,21.89,"0",0.624,6.335,98.2,2.1107,4,437,21.2,394.67,16.96,18.1
138 | 0.32264,0,21.89,"0",0.624,5.942,93.5,1.9669,4,437,21.2,378.25,16.9,17.4
139 | 0.35233,0,21.89,"0",0.624,6.454,98.4,1.8498,4,437,21.2,394.08,14.59,17.1
140 | 0.2498,0,21.89,"0",0.624,5.857,98.2,1.6686,4,437,21.2,392.04,21.32,13.3
141 | 0.54452,0,21.89,"0",0.624,6.151,97.9,1.6687,4,437,21.2,396.9,18.46,17.8
142 | 0.2909,0,21.89,"0",0.624,6.174,93.6,1.6119,4,437,21.2,388.08,24.16,14
143 | 1.62864,0,21.89,"0",0.624,5.019,100,1.4394,4,437,21.2,396.9,34.41,14.4
144 | 3.32105,0,19.58,"1",0.871,5.403,100,1.3216,5,403,14.7,396.9,26.82,13.4
145 | 4.0974,0,19.58,"0",0.871,5.468,100,1.4118,5,403,14.7,396.9,26.42,15.6
146 | 2.77974,0,19.58,"0",0.871,4.903,97.8,1.3459,5,403,14.7,396.9,29.29,11.8
147 | 2.37934,0,19.58,"0",0.871,6.13,100,1.4191,5,403,14.7,172.91,27.8,13.8
148 | 2.15505,0,19.58,"0",0.871,5.628,100,1.5166,5,403,14.7,169.27,16.65,15.6
149 | 2.36862,0,19.58,"0",0.871,4.926,95.7,1.4608,5,403,14.7,391.71,29.53,14.6
150 | 2.33099,0,19.58,"0",0.871,5.186,93.8,1.5296,5,403,14.7,356.99,28.32,17.8
151 | 2.73397,0,19.58,"0",0.871,5.597,94.9,1.5257,5,403,14.7,351.85,21.45,15.4
152 | 1.6566,0,19.58,"0",0.871,6.122,97.3,1.618,5,403,14.7,372.8,14.1,21.5
153 | 1.49632,0,19.58,"0",0.871,5.404,100,1.5916,5,403,14.7,341.6,13.28,19.6
154 | 1.12658,0,19.58,"1",0.871,5.012,88,1.6102,5,403,14.7,343.28,12.12,15.3
155 | 2.14918,0,19.58,"0",0.871,5.709,98.5,1.6232,5,403,14.7,261.95,15.79,19.4
156 | 1.41385,0,19.58,"1",0.871,6.129,96,1.7494,5,403,14.7,321.02,15.12,17
157 | 3.53501,0,19.58,"1",0.871,6.152,82.6,1.7455,5,403,14.7,88.01,15.02,15.6
158 | 2.44668,0,19.58,"0",0.871,5.272,94,1.7364,5,403,14.7,88.63,16.14,13.1
159 | 1.22358,0,19.58,"0",0.605,6.943,97.4,1.8773,5,403,14.7,363.43,4.59,41.3
160 | 1.34284,0,19.58,"0",0.605,6.066,100,1.7573,5,403,14.7,353.89,6.43,24.3
161 | 1.42502,0,19.58,"0",0.871,6.51,100,1.7659,5,403,14.7,364.31,7.39,23.3
162 | 1.27346,0,19.58,"1",0.605,6.25,92.6,1.7984,5,403,14.7,338.92,5.5,27
163 | 1.46336,0,19.58,"0",0.605,7.489,90.8,1.9709,5,403,14.7,374.43,1.73,50
164 | 1.83377,0,19.58,"1",0.605,7.802,98.2,2.0407,5,403,14.7,389.61,1.92,50
165 | 1.51902,0,19.58,"1",0.605,8.375,93.9,2.162,5,403,14.7,388.45,3.32,50
166 | 2.24236,0,19.58,"0",0.605,5.854,91.8,2.422,5,403,14.7,395.11,11.64,22.7
167 | 2.924,0,19.58,"0",0.605,6.101,93,2.2834,5,403,14.7,240.16,9.81,25
168 | 2.01019,0,19.58,"0",0.605,7.929,96.2,2.0459,5,403,14.7,369.3,3.7,50
169 | 1.80028,0,19.58,"0",0.605,5.877,79.2,2.4259,5,403,14.7,227.61,12.14,23.8
170 | 2.3004,0,19.58,"0",0.605,6.319,96.1,2.1,5,403,14.7,297.09,11.1,23.8
171 | 2.44953,0,19.58,"0",0.605,6.402,95.2,2.2625,5,403,14.7,330.04,11.32,22.3
172 | 1.20742,0,19.58,"0",0.605,5.875,94.6,2.4259,5,403,14.7,292.29,14.43,17.4
173 | 2.3139,0,19.58,"0",0.605,5.88,97.3,2.3887,5,403,14.7,348.13,12.03,19.1
174 | 0.13914,0,4.05,"0",0.51,5.572,88.5,2.5961,5,296,16.6,396.9,14.69,23.1
175 | 0.09178,0,4.05,"0",0.51,6.416,84.1,2.6463,5,296,16.6,395.5,9.04,23.6
176 | 0.08447,0,4.05,"0",0.51,5.859,68.7,2.7019,5,296,16.6,393.23,9.64,22.6
177 | 0.06664,0,4.05,"0",0.51,6.546,33.1,3.1323,5,296,16.6,390.96,5.33,29.4
178 | 0.07022,0,4.05,"0",0.51,6.02,47.2,3.5549,5,296,16.6,393.23,10.11,23.2
179 | 0.05425,0,4.05,"0",0.51,6.315,73.4,3.3175,5,296,16.6,395.6,6.29,24.6
180 | 0.06642,0,4.05,"0",0.51,6.86,74.4,2.9153,5,296,16.6,391.27,6.92,29.9
181 | 0.0578,0,2.46,"0",0.488,6.98,58.4,2.829,3,193,17.8,396.9,5.04,37.2
182 | 0.06588,0,2.46,"0",0.488,7.765,83.3,2.741,3,193,17.8,395.56,7.56,39.8
183 | 0.06888,0,2.46,"0",0.488,6.144,62.2,2.5979,3,193,17.8,396.9,9.45,36.2
184 | 0.09103,0,2.46,"0",0.488,7.155,92.2,2.7006,3,193,17.8,394.12,4.82,37.9
185 | 0.10008,0,2.46,"0",0.488,6.563,95.6,2.847,3,193,17.8,396.9,5.68,32.5
186 | 0.08308,0,2.46,"0",0.488,5.604,89.8,2.9879,3,193,17.8,391,13.98,26.4
187 | 0.06047,0,2.46,"0",0.488,6.153,68.8,3.2797,3,193,17.8,387.11,13.15,29.6
188 | 0.05602,0,2.46,"0",0.488,7.831,53.6,3.1992,3,193,17.8,392.63,4.45,50
189 | 0.07875,45,3.44,"0",0.437,6.782,41.1,3.7886,5,398,15.2,393.87,6.68,32
190 | 0.12579,45,3.44,"0",0.437,6.556,29.1,4.5667,5,398,15.2,382.84,4.56,29.8
191 | 0.0837,45,3.44,"0",0.437,7.185,38.9,4.5667,5,398,15.2,396.9,5.39,34.9
192 | 0.09068,45,3.44,"0",0.437,6.951,21.5,6.4798,5,398,15.2,377.68,5.1,37
193 | 0.06911,45,3.44,"0",0.437,6.739,30.8,6.4798,5,398,15.2,389.71,4.69,30.5
194 | 0.08664,45,3.44,"0",0.437,7.178,26.3,6.4798,5,398,15.2,390.49,2.87,36.4
195 | 0.02187,60,2.93,"0",0.401,6.8,9.9,6.2196,1,265,15.6,393.37,5.03,31.1
196 | 0.01439,60,2.93,"0",0.401,6.604,18.8,6.2196,1,265,15.6,376.7,4.38,29.1
197 | 0.01381,80,0.46,"0",0.422,7.875,32,5.6484,4,255,14.4,394.23,2.97,50
198 | 0.04011,80,1.52,"0",0.404,7.287,34.1,7.309,2,329,12.6,396.9,4.08,33.3
199 | 0.04666,80,1.52,"0",0.404,7.107,36.6,7.309,2,329,12.6,354.31,8.61,30.3
200 | 0.03768,80,1.52,"0",0.404,7.274,38.3,7.309,2,329,12.6,392.2,6.62,34.6
201 | 0.0315,95,1.47,"0",0.403,6.975,15.3,7.6534,3,402,17,396.9,4.56,34.9
202 | 0.01778,95,1.47,"0",0.403,7.135,13.9,7.6534,3,402,17,384.3,4.45,32.9
203 | 0.03445,82.5,2.03,"0",0.415,6.162,38.4,6.27,2,348,14.7,393.77,7.43,24.1
204 | 0.02177,82.5,2.03,"0",0.415,7.61,15.7,6.27,2,348,14.7,395.38,3.11,42.3
205 | 0.0351,95,2.68,"0",0.4161,7.853,33.2,5.118,4,224,14.7,392.78,3.81,48.5
206 | 0.02009,95,2.68,"0",0.4161,8.034,31.9,5.118,4,224,14.7,390.55,2.88,50
207 | 0.13642,0,10.59,"0",0.489,5.891,22.3,3.9454,4,277,18.6,396.9,10.87,22.6
208 | 0.22969,0,10.59,"0",0.489,6.326,52.5,4.3549,4,277,18.6,394.87,10.97,24.4
209 | 0.25199,0,10.59,"0",0.489,5.783,72.7,4.3549,4,277,18.6,389.43,18.06,22.5
210 | 0.13587,0,10.59,"1",0.489,6.064,59.1,4.2392,4,277,18.6,381.32,14.66,24.4
211 | 0.43571,0,10.59,"1",0.489,5.344,100,3.875,4,277,18.6,396.9,23.09,20
212 | 0.17446,0,10.59,"1",0.489,5.96,92.1,3.8771,4,277,18.6,393.25,17.27,21.7
213 | 0.37578,0,10.59,"1",0.489,5.404,88.6,3.665,4,277,18.6,395.24,23.98,19.3
214 | 0.21719,0,10.59,"1",0.489,5.807,53.8,3.6526,4,277,18.6,390.94,16.03,22.4
215 | 0.14052,0,10.59,"0",0.489,6.375,32.3,3.9454,4,277,18.6,385.81,9.38,28.1
216 | 0.28955,0,10.59,"0",0.489,5.412,9.8,3.5875,4,277,18.6,348.93,29.55,23.7
217 | 0.19802,0,10.59,"0",0.489,6.182,42.4,3.9454,4,277,18.6,393.63,9.47,25
218 | 0.0456,0,13.89,"1",0.55,5.888,56,3.1121,5,276,16.4,392.8,13.51,23.3
219 | 0.07013,0,13.89,"0",0.55,6.642,85.1,3.4211,5,276,16.4,392.78,9.69,28.7
220 | 0.11069,0,13.89,"1",0.55,5.951,93.8,2.8893,5,276,16.4,396.9,17.92,21.5
221 | 0.11425,0,13.89,"1",0.55,6.373,92.4,3.3633,5,276,16.4,393.74,10.5,23
222 | 0.35809,0,6.2,"1",0.507,6.951,88.5,2.8617,8,307,17.4,391.7,9.71,26.7
223 | 0.40771,0,6.2,"1",0.507,6.164,91.3,3.048,8,307,17.4,395.24,21.46,21.7
224 | 0.62356,0,6.2,"1",0.507,6.879,77.7,3.2721,8,307,17.4,390.39,9.93,27.5
225 | 0.6147,0,6.2,"0",0.507,6.618,80.8,3.2721,8,307,17.4,396.9,7.6,30.1
226 | 0.31533,0,6.2,"0",0.504,8.266,78.3,2.8944,8,307,17.4,385.05,4.14,44.8
227 | 0.52693,0,6.2,"0",0.504,8.725,83,2.8944,8,307,17.4,382,4.63,50
228 | 0.38214,0,6.2,"0",0.504,8.04,86.5,3.2157,8,307,17.4,387.38,3.13,37.6
229 | 0.41238,0,6.2,"0",0.504,7.163,79.9,3.2157,8,307,17.4,372.08,6.36,31.6
230 | 0.29819,0,6.2,"0",0.504,7.686,17,3.3751,8,307,17.4,377.51,3.92,46.7
231 | 0.44178,0,6.2,"0",0.504,6.552,21.4,3.3751,8,307,17.4,380.34,3.76,31.5
232 | 0.537,0,6.2,"0",0.504,5.981,68.1,3.6715,8,307,17.4,378.35,11.65,24.3
233 | 0.46296,0,6.2,"0",0.504,7.412,76.9,3.6715,8,307,17.4,376.14,5.25,31.7
234 | 0.57529,0,6.2,"0",0.507,8.337,73.3,3.8384,8,307,17.4,385.91,2.47,41.7
235 | 0.33147,0,6.2,"0",0.507,8.247,70.4,3.6519,8,307,17.4,378.95,3.95,48.3
236 | 0.44791,0,6.2,"1",0.507,6.726,66.5,3.6519,8,307,17.4,360.2,8.05,29
237 | 0.33045,0,6.2,"0",0.507,6.086,61.5,3.6519,8,307,17.4,376.75,10.88,24
238 | 0.52058,0,6.2,"1",0.507,6.631,76.5,4.148,8,307,17.4,388.45,9.54,25.1
239 | 0.51183,0,6.2,"0",0.507,7.358,71.6,4.148,8,307,17.4,390.07,4.73,31.5
240 | 0.08244,30,4.93,"0",0.428,6.481,18.5,6.1899,6,300,16.6,379.41,6.36,23.7
241 | 0.09252,30,4.93,"0",0.428,6.606,42.2,6.1899,6,300,16.6,383.78,7.37,23.3
242 | 0.11329,30,4.93,"0",0.428,6.897,54.3,6.3361,6,300,16.6,391.25,11.38,22
243 | 0.10612,30,4.93,"0",0.428,6.095,65.1,6.3361,6,300,16.6,394.62,12.4,20.1
244 | 0.1029,30,4.93,"0",0.428,6.358,52.9,7.0355,6,300,16.6,372.75,11.22,22.2
245 | 0.12757,30,4.93,"0",0.428,6.393,7.8,7.0355,6,300,16.6,374.71,5.19,23.7
246 | 0.20608,22,5.86,"0",0.431,5.593,76.5,7.9549,7,330,19.1,372.49,12.5,17.6
247 | 0.19133,22,5.86,"0",0.431,5.605,70.2,7.9549,7,330,19.1,389.13,18.46,18.5
248 | 0.33983,22,5.86,"0",0.431,6.108,34.9,8.0555,7,330,19.1,390.18,9.16,24.3
249 | 0.19657,22,5.86,"0",0.431,6.226,79.2,8.0555,7,330,19.1,376.14,10.15,20.5
250 | 0.16439,22,5.86,"0",0.431,6.433,49.1,7.8265,7,330,19.1,374.71,9.52,24.5
251 | 0.19073,22,5.86,"0",0.431,6.718,17.5,7.8265,7,330,19.1,393.74,6.56,26.2
252 | 0.1403,22,5.86,"0",0.431,6.487,13,7.3967,7,330,19.1,396.28,5.9,24.4
253 | 0.21409,22,5.86,"0",0.431,6.438,8.9,7.3967,7,330,19.1,377.07,3.59,24.8
254 | 0.08221,22,5.86,"0",0.431,6.957,6.8,8.9067,7,330,19.1,386.09,3.53,29.6
255 | 0.36894,22,5.86,"0",0.431,8.259,8.4,8.9067,7,330,19.1,396.9,3.54,42.8
256 | 0.04819,80,3.64,"0",0.392,6.108,32,9.2203,1,315,16.4,392.89,6.57,21.9
257 | 0.03548,80,3.64,"0",0.392,5.876,19.1,9.2203,1,315,16.4,395.18,9.25,20.9
258 | 0.01538,90,3.75,"0",0.394,7.454,34.2,6.3361,3,244,15.9,386.34,3.11,44
259 | 0.61154,20,3.97,"0",0.647,8.704,86.9,1.801,5,264,13,389.7,5.12,50
260 | 0.66351,20,3.97,"0",0.647,7.333,100,1.8946,5,264,13,383.29,7.79,36
261 | 0.65665,20,3.97,"0",0.647,6.842,100,2.0107,5,264,13,391.93,6.9,30.1
262 | 0.54011,20,3.97,"0",0.647,7.203,81.8,2.1121,5,264,13,392.8,9.59,33.8
263 | 0.53412,20,3.97,"0",0.647,7.52,89.4,2.1398,5,264,13,388.37,7.26,43.1
264 | 0.52014,20,3.97,"0",0.647,8.398,91.5,2.2885,5,264,13,386.86,5.91,48.8
265 | 0.82526,20,3.97,"0",0.647,7.327,94.5,2.0788,5,264,13,393.42,11.25,31
266 | 0.55007,20,3.97,"0",0.647,7.206,91.6,1.9301,5,264,13,387.89,8.1,36.5
267 | 0.76162,20,3.97,"0",0.647,5.56,62.8,1.9865,5,264,13,392.4,10.45,22.8
268 | 0.7857,20,3.97,"0",0.647,7.014,84.6,2.1329,5,264,13,384.07,14.79,30.7
269 | 0.57834,20,3.97,"0",0.575,8.297,67,2.4216,5,264,13,384.54,7.44,50
270 | 0.5405,20,3.97,"0",0.575,7.47,52.6,2.872,5,264,13,390.3,3.16,43.5
271 | 0.09065,20,6.96,"1",0.464,5.92,61.5,3.9175,3,223,18.6,391.34,13.65,20.7
272 | 0.29916,20,6.96,"0",0.464,5.856,42.1,4.429,3,223,18.6,388.65,13,21.1
273 | 0.16211,20,6.96,"0",0.464,6.24,16.3,4.429,3,223,18.6,396.9,6.59,25.2
274 | 0.1146,20,6.96,"0",0.464,6.538,58.7,3.9175,3,223,18.6,394.96,7.73,24.4
275 | 0.22188,20,6.96,"1",0.464,7.691,51.8,4.3665,3,223,18.6,390.77,6.58,35.2
276 | 0.05644,40,6.41,"1",0.447,6.758,32.9,4.0776,4,254,17.6,396.9,3.53,32.4
277 | 0.09604,40,6.41,"0",0.447,6.854,42.8,4.2673,4,254,17.6,396.9,2.98,32
278 | 0.10469,40,6.41,"1",0.447,7.267,49,4.7872,4,254,17.6,389.25,6.05,33.2
279 | 0.06127,40,6.41,"1",0.447,6.826,27.6,4.8628,4,254,17.6,393.45,4.16,33.1
280 | 0.07978,40,6.41,"0",0.447,6.482,32.1,4.1403,4,254,17.6,396.9,7.19,29.1
281 | 0.21038,20,3.33,"0",0.4429,6.812,32.2,4.1007,5,216,14.9,396.9,4.85,35.1
282 | 0.03578,20,3.33,"0",0.4429,7.82,64.5,4.6947,5,216,14.9,387.31,3.76,45.4
283 | 0.03705,20,3.33,"0",0.4429,6.968,37.2,5.2447,5,216,14.9,392.23,4.59,35.4
284 | 0.06129,20,3.33,"1",0.4429,7.645,49.7,5.2119,5,216,14.9,377.07,3.01,46
285 | 0.01501,90,1.21,"1",0.401,7.923,24.8,5.885,1,198,13.6,395.52,3.16,50
286 | 0.00906,90,2.97,"0",0.4,7.088,20.8,7.3073,1,285,15.3,394.72,7.85,32.2
287 | 0.01096,55,2.25,"0",0.389,6.453,31.9,7.3073,1,300,15.3,394.72,8.23,22
288 | 0.01965,80,1.76,"0",0.385,6.23,31.5,9.0892,1,241,18.2,341.6,12.93,20.1
289 | 0.03871,52.5,5.32,"0",0.405,6.209,31.3,7.3172,6,293,16.6,396.9,7.14,23.2
290 | 0.0459,52.5,5.32,"0",0.405,6.315,45.6,7.3172,6,293,16.6,396.9,7.6,22.3
291 | 0.04297,52.5,5.32,"0",0.405,6.565,22.9,7.3172,6,293,16.6,371.72,9.51,24.8
292 | 0.03502,80,4.95,"0",0.411,6.861,27.9,5.1167,4,245,19.2,396.9,3.33,28.5
293 | 0.07886,80,4.95,"0",0.411,7.148,27.7,5.1167,4,245,19.2,396.9,3.56,37.3
294 | 0.03615,80,4.95,"0",0.411,6.63,23.4,5.1167,4,245,19.2,396.9,4.7,27.9
295 | 0.08265,0,13.92,"0",0.437,6.127,18.4,5.5027,4,289,16,396.9,8.58,23.9
296 | 0.08199,0,13.92,"0",0.437,6.009,42.3,5.5027,4,289,16,396.9,10.4,21.7
297 | 0.12932,0,13.92,"0",0.437,6.678,31.1,5.9604,4,289,16,396.9,6.27,28.6
298 | 0.05372,0,13.92,"0",0.437,6.549,51,5.9604,4,289,16,392.85,7.39,27.1
299 | 0.14103,0,13.92,"0",0.437,5.79,58,6.32,4,289,16,396.9,15.84,20.3
300 | 0.06466,70,2.24,"0",0.4,6.345,20.1,7.8278,5,358,14.8,368.24,4.97,22.5
301 | 0.05561,70,2.24,"0",0.4,7.041,10,7.8278,5,358,14.8,371.58,4.74,29
302 | 0.04417,70,2.24,"0",0.4,6.871,47.4,7.8278,5,358,14.8,390.86,6.07,24.8
303 | 0.03537,34,6.09,"0",0.433,6.59,40.4,5.4917,7,329,16.1,395.75,9.5,22
304 | 0.09266,34,6.09,"0",0.433,6.495,18.4,5.4917,7,329,16.1,383.61,8.67,26.4
305 | 0.1,34,6.09,"0",0.433,6.982,17.7,5.4917,7,329,16.1,390.43,4.86,33.1
306 | 0.05515,33,2.18,"0",0.472,7.236,41.1,4.022,7,222,18.4,393.68,6.93,36.1
307 | 0.05479,33,2.18,"0",0.472,6.616,58.1,3.37,7,222,18.4,393.36,8.93,28.4
308 | 0.07503,33,2.18,"0",0.472,7.42,71.9,3.0992,7,222,18.4,396.9,6.47,33.4
309 | 0.04932,33,2.18,"0",0.472,6.849,70.3,3.1827,7,222,18.4,396.9,7.53,28.2
310 | 0.49298,0,9.9,"0",0.544,6.635,82.5,3.3175,4,304,18.4,396.9,4.54,22.8
311 | 0.3494,0,9.9,"0",0.544,5.972,76.7,3.1025,4,304,18.4,396.24,9.97,20.3
312 | 2.63548,0,9.9,"0",0.544,4.973,37.8,2.5194,4,304,18.4,350.45,12.64,16.1
313 | 0.79041,0,9.9,"0",0.544,6.122,52.8,2.6403,4,304,18.4,396.9,5.98,22.1
314 | 0.26169,0,9.9,"0",0.544,6.023,90.4,2.834,4,304,18.4,396.3,11.72,19.4
315 | 0.26938,0,9.9,"0",0.544,6.266,82.8,3.2628,4,304,18.4,393.39,7.9,21.6
316 | 0.3692,0,9.9,"0",0.544,6.567,87.3,3.6023,4,304,18.4,395.69,9.28,23.8
317 | 0.25356,0,9.9,"0",0.544,5.705,77.7,3.945,4,304,18.4,396.42,11.5,16.2
318 | 0.31827,0,9.9,"0",0.544,5.914,83.2,3.9986,4,304,18.4,390.7,18.33,17.8
319 | 0.24522,0,9.9,"0",0.544,5.782,71.7,4.0317,4,304,18.4,396.9,15.94,19.8
320 | 0.40202,0,9.9,"0",0.544,6.382,67.2,3.5325,4,304,18.4,395.21,10.36,23.1
321 | 0.47547,0,9.9,"0",0.544,6.113,58.8,4.0019,4,304,18.4,396.23,12.73,21
322 | 0.1676,0,7.38,"0",0.493,6.426,52.3,4.5404,5,287,19.6,396.9,7.2,23.8
323 | 0.18159,0,7.38,"0",0.493,6.376,54.3,4.5404,5,287,19.6,396.9,6.87,23.1
324 | 0.35114,0,7.38,"0",0.493,6.041,49.9,4.7211,5,287,19.6,396.9,7.7,20.4
325 | 0.28392,0,7.38,"0",0.493,5.708,74.3,4.7211,5,287,19.6,391.13,11.74,18.5
326 | 0.34109,0,7.38,"0",0.493,6.415,40.1,4.7211,5,287,19.6,396.9,6.12,25
327 | 0.19186,0,7.38,"0",0.493,6.431,14.7,5.4159,5,287,19.6,393.68,5.08,24.6
328 | 0.30347,0,7.38,"0",0.493,6.312,28.9,5.4159,5,287,19.6,396.9,6.15,23
329 | 0.24103,0,7.38,"0",0.493,6.083,43.7,5.4159,5,287,19.6,396.9,12.79,22.2
330 | 0.06617,0,3.24,"0",0.46,5.868,25.8,5.2146,4,430,16.9,382.44,9.97,19.3
331 | 0.06724,0,3.24,"0",0.46,6.333,17.2,5.2146,4,430,16.9,375.21,7.34,22.6
332 | 0.04544,0,3.24,"0",0.46,6.144,32.2,5.8736,4,430,16.9,368.57,9.09,19.8
333 | 0.05023,35,6.06,"0",0.4379,5.706,28.4,6.6407,1,304,16.9,394.02,12.43,17.1
334 | 0.03466,35,6.06,"0",0.4379,6.031,23.3,6.6407,1,304,16.9,362.25,7.83,19.4
335 | 0.05083,0,5.19,"0",0.515,6.316,38.1,6.4584,5,224,20.2,389.71,5.68,22.2
336 | 0.03738,0,5.19,"0",0.515,6.31,38.5,6.4584,5,224,20.2,389.4,6.75,20.7
337 | 0.03961,0,5.19,"0",0.515,6.037,34.5,5.9853,5,224,20.2,396.9,8.01,21.1
338 | 0.03427,0,5.19,"0",0.515,5.869,46.3,5.2311,5,224,20.2,396.9,9.8,19.5
339 | 0.03041,0,5.19,"0",0.515,5.895,59.6,5.615,5,224,20.2,394.81,10.56,18.5
340 | 0.03306,0,5.19,"0",0.515,6.059,37.3,4.8122,5,224,20.2,396.14,8.51,20.6
341 | 0.05497,0,5.19,"0",0.515,5.985,45.4,4.8122,5,224,20.2,396.9,9.74,19
342 | 0.06151,0,5.19,"0",0.515,5.968,58.5,4.8122,5,224,20.2,396.9,9.29,18.7
343 | 0.01301,35,1.52,"0",0.442,7.241,49.3,7.0379,1,284,15.5,394.74,5.49,32.7
344 | 0.02498,0,1.89,"0",0.518,6.54,59.7,6.2669,1,422,15.9,389.96,8.65,16.5
345 | 0.02543,55,3.78,"0",0.484,6.696,56.4,5.7321,5,370,17.6,396.9,7.18,23.9
346 | 0.03049,55,3.78,"0",0.484,6.874,28.1,6.4654,5,370,17.6,387.97,4.61,31.2
347 | 0.03113,0,4.39,"0",0.442,6.014,48.5,8.0136,3,352,18.8,385.64,10.53,17.5
348 | 0.06162,0,4.39,"0",0.442,5.898,52.3,8.0136,3,352,18.8,364.61,12.67,17.2
349 | 0.0187,85,4.15,"0",0.429,6.516,27.7,8.5353,4,351,17.9,392.43,6.36,23.1
350 | 0.01501,80,2.01,"0",0.435,6.635,29.7,8.344,4,280,17,390.94,5.99,24.5
351 | 0.02899,40,1.25,"0",0.429,6.939,34.5,8.7921,1,335,19.7,389.85,5.89,26.6
352 | 0.06211,40,1.25,"0",0.429,6.49,44.4,8.7921,1,335,19.7,396.9,5.98,22.9
353 | 0.0795,60,1.69,"0",0.411,6.579,35.9,10.7103,4,411,18.3,370.78,5.49,24.1
354 | 0.07244,60,1.69,"0",0.411,5.884,18.5,10.7103,4,411,18.3,392.33,7.79,18.6
355 | 0.01709,90,2.02,"0",0.41,6.728,36.1,12.1265,5,187,17,384.46,4.5,30.1
356 | 0.04301,80,1.91,"0",0.413,5.663,21.9,10.5857,4,334,22,382.8,8.05,18.2
357 | 0.10659,80,1.91,"0",0.413,5.936,19.5,10.5857,4,334,22,376.04,5.57,20.6
358 | 8.98296,0,18.1,"1",0.77,6.212,97.4,2.1222,24,666,20.2,377.73,17.6,17.8
359 | 3.8497,0,18.1,"1",0.77,6.395,91,2.5052,24,666,20.2,391.34,13.27,21.7
360 | 5.20177,0,18.1,"1",0.77,6.127,83.4,2.7227,24,666,20.2,395.43,11.48,22.7
361 | 4.26131,0,18.1,"0",0.77,6.112,81.3,2.5091,24,666,20.2,390.74,12.67,22.6
362 | 4.54192,0,18.1,"0",0.77,6.398,88,2.5182,24,666,20.2,374.56,7.79,25
363 | 3.83684,0,18.1,"0",0.77,6.251,91.1,2.2955,24,666,20.2,350.65,14.19,19.9
364 | 3.67822,0,18.1,"0",0.77,5.362,96.2,2.1036,24,666,20.2,380.79,10.19,20.8
365 | 4.22239,0,18.1,"1",0.77,5.803,89,1.9047,24,666,20.2,353.04,14.64,16.8
366 | 3.47428,0,18.1,"1",0.718,8.78,82.9,1.9047,24,666,20.2,354.55,5.29,21.9
367 | 4.55587,0,18.1,"0",0.718,3.561,87.9,1.6132,24,666,20.2,354.7,7.12,27.5
368 | 3.69695,0,18.1,"0",0.718,4.963,91.4,1.7523,24,666,20.2,316.03,14,21.9
369 | 13.5222,0,18.1,"0",0.631,3.863,100,1.5106,24,666,20.2,131.42,13.33,23.1
370 | 4.89822,0,18.1,"0",0.631,4.97,100,1.3325,24,666,20.2,375.52,3.26,50
371 | 5.66998,0,18.1,"1",0.631,6.683,96.8,1.3567,24,666,20.2,375.33,3.73,50
372 | 6.53876,0,18.1,"1",0.631,7.016,97.5,1.2024,24,666,20.2,392.05,2.96,50
373 | 9.2323,0,18.1,"0",0.631,6.216,100,1.1691,24,666,20.2,366.15,9.53,50
374 | 8.26725,0,18.1,"1",0.668,5.875,89.6,1.1296,24,666,20.2,347.88,8.88,50
375 | 11.1081,0,18.1,"0",0.668,4.906,100,1.1742,24,666,20.2,396.9,34.77,13.8
376 | 18.4982,0,18.1,"0",0.668,4.138,100,1.137,24,666,20.2,396.9,37.97,13.8
377 | 19.6091,0,18.1,"0",0.671,7.313,97.9,1.3163,24,666,20.2,396.9,13.44,15
378 | 15.288,0,18.1,"0",0.671,6.649,93.3,1.3449,24,666,20.2,363.02,23.24,13.9
379 | 9.82349,0,18.1,"0",0.671,6.794,98.8,1.358,24,666,20.2,396.9,21.24,13.3
380 | 23.6482,0,18.1,"0",0.671,6.38,96.2,1.3861,24,666,20.2,396.9,23.69,13.1
381 | 17.8667,0,18.1,"0",0.671,6.223,100,1.3861,24,666,20.2,393.74,21.78,10.2
382 | 88.9762,0,18.1,"0",0.671,6.968,91.9,1.4165,24,666,20.2,396.9,17.21,10.4
383 | 15.8744,0,18.1,"0",0.671,6.545,99.1,1.5192,24,666,20.2,396.9,21.08,10.9
384 | 9.18702,0,18.1,"0",0.7,5.536,100,1.5804,24,666,20.2,396.9,23.6,11.3
385 | 7.99248,0,18.1,"0",0.7,5.52,100,1.5331,24,666,20.2,396.9,24.56,12.3
386 | 20.0849,0,18.1,"0",0.7,4.368,91.2,1.4395,24,666,20.2,285.83,30.63,8.8
387 | 16.8118,0,18.1,"0",0.7,5.277,98.1,1.4261,24,666,20.2,396.9,30.81,7.2
388 | 24.3938,0,18.1,"0",0.7,4.652,100,1.4672,24,666,20.2,396.9,28.28,10.5
389 | 22.5971,0,18.1,"0",0.7,5,89.5,1.5184,24,666,20.2,396.9,31.99,7.4
390 | 14.3337,0,18.1,"0",0.7,4.88,100,1.5895,24,666,20.2,372.92,30.62,10.2
391 | 8.15174,0,18.1,"0",0.7,5.39,98.9,1.7281,24,666,20.2,396.9,20.85,11.5
392 | 6.96215,0,18.1,"0",0.7,5.713,97,1.9265,24,666,20.2,394.43,17.11,15.1
393 | 5.29305,0,18.1,"0",0.7,6.051,82.5,2.1678,24,666,20.2,378.38,18.76,23.2
394 | 11.5779,0,18.1,"0",0.7,5.036,97,1.77,24,666,20.2,396.9,25.68,9.7
395 | 8.64476,0,18.1,"0",0.693,6.193,92.6,1.7912,24,666,20.2,396.9,15.17,13.8
396 | 13.3598,0,18.1,"0",0.693,5.887,94.7,1.7821,24,666,20.2,396.9,16.35,12.7
397 | 8.71675,0,18.1,"0",0.693,6.471,98.8,1.7257,24,666,20.2,391.98,17.12,13.1
398 | 5.87205,0,18.1,"0",0.693,6.405,96,1.6768,24,666,20.2,396.9,19.37,12.5
399 | 7.67202,0,18.1,"0",0.693,5.747,98.9,1.6334,24,666,20.2,393.1,19.92,8.5
400 | 38.3518,0,18.1,"0",0.693,5.453,100,1.4896,24,666,20.2,396.9,30.59,5
401 | 9.91655,0,18.1,"0",0.693,5.852,77.8,1.5004,24,666,20.2,338.16,29.97,6.3
402 | 25.0461,0,18.1,"0",0.693,5.987,100,1.5888,24,666,20.2,396.9,26.77,5.6
403 | 14.2362,0,18.1,"0",0.693,6.343,100,1.5741,24,666,20.2,396.9,20.32,7.2
404 | 9.59571,0,18.1,"0",0.693,6.404,100,1.639,24,666,20.2,376.11,20.31,12.1
405 | 24.8017,0,18.1,"0",0.693,5.349,96,1.7028,24,666,20.2,396.9,19.77,8.3
406 | 41.5292,0,18.1,"0",0.693,5.531,85.4,1.6074,24,666,20.2,329.46,27.38,8.5
407 | 67.9208,0,18.1,"0",0.693,5.683,100,1.4254,24,666,20.2,384.97,22.98,5
408 | 20.7162,0,18.1,"0",0.659,4.138,100,1.1781,24,666,20.2,370.22,23.34,11.9
409 | 11.9511,0,18.1,"0",0.659,5.608,100,1.2852,24,666,20.2,332.09,12.13,27.9
410 | 7.40389,0,18.1,"0",0.597,5.617,97.9,1.4547,24,666,20.2,314.64,26.4,17.2
411 | 14.4383,0,18.1,"0",0.597,6.852,100,1.4655,24,666,20.2,179.36,19.78,27.5
412 | 51.1358,0,18.1,"0",0.597,5.757,100,1.413,24,666,20.2,2.6,10.11,15
413 | 14.0507,0,18.1,"0",0.597,6.657,100,1.5275,24,666,20.2,35.05,21.22,17.2
414 | 18.811,0,18.1,"0",0.597,4.628,100,1.5539,24,666,20.2,28.79,34.37,17.9
415 | 28.6558,0,18.1,"0",0.597,5.155,100,1.5894,24,666,20.2,210.97,20.08,16.3
416 | 45.7461,0,18.1,"0",0.693,4.519,100,1.6582,24,666,20.2,88.27,36.98,7
417 | 18.0846,0,18.1,"0",0.679,6.434,100,1.8347,24,666,20.2,27.25,29.05,7.2
418 | 10.8342,0,18.1,"0",0.679,6.782,90.8,1.8195,24,666,20.2,21.57,25.79,7.5
419 | 25.9406,0,18.1,"0",0.679,5.304,89.1,1.6475,24,666,20.2,127.36,26.64,10.4
420 | 73.5341,0,18.1,"0",0.679,5.957,100,1.8026,24,666,20.2,16.45,20.62,8.8
421 | 11.8123,0,18.1,"0",0.718,6.824,76.5,1.794,24,666,20.2,48.45,22.74,8.4
422 | 11.0874,0,18.1,"0",0.718,6.411,100,1.8589,24,666,20.2,318.75,15.02,16.7
423 | 7.02259,0,18.1,"0",0.718,6.006,95.3,1.8746,24,666,20.2,319.98,15.7,14.2
424 | 12.0482,0,18.1,"0",0.614,5.648,87.6,1.9512,24,666,20.2,291.55,14.1,20.8
425 | 7.05042,0,18.1,"0",0.614,6.103,85.1,2.0218,24,666,20.2,2.52,23.29,13.4
426 | 8.79212,0,18.1,"0",0.584,5.565,70.6,2.0635,24,666,20.2,3.65,17.16,11.7
427 | 15.8603,0,18.1,"0",0.679,5.896,95.4,1.9096,24,666,20.2,7.68,24.39,8.3
428 | 12.2472,0,18.1,"0",0.584,5.837,59.7,1.9976,24,666,20.2,24.65,15.69,10.2
429 | 37.6619,0,18.1,"0",0.679,6.202,78.7,1.8629,24,666,20.2,18.82,14.52,10.9
430 | 7.36711,0,18.1,"0",0.679,6.193,78.1,1.9356,24,666,20.2,96.73,21.52,11
431 | 9.33889,0,18.1,"0",0.679,6.38,95.6,1.9682,24,666,20.2,60.72,24.08,9.5
432 | 8.49213,0,18.1,"0",0.584,6.348,86.1,2.0527,24,666,20.2,83.45,17.64,14.5
433 | 10.0623,0,18.1,"0",0.584,6.833,94.3,2.0882,24,666,20.2,81.33,19.69,14.1
434 | 6.44405,0,18.1,"0",0.584,6.425,74.8,2.2004,24,666,20.2,97.95,12.03,16.1
435 | 5.58107,0,18.1,"0",0.713,6.436,87.9,2.3158,24,666,20.2,100.19,16.22,14.3
436 | 13.9134,0,18.1,"0",0.713,6.208,95,2.2222,24,666,20.2,100.63,15.17,11.7
437 | 11.1604,0,18.1,"0",0.74,6.629,94.6,2.1247,24,666,20.2,109.85,23.27,13.4
438 | 14.4208,0,18.1,"0",0.74,6.461,93.3,2.0026,24,666,20.2,27.49,18.05,9.6
439 | 15.1772,0,18.1,"0",0.74,6.152,100,1.9142,24,666,20.2,9.32,26.45,8.7
440 | 13.6781,0,18.1,"0",0.74,5.935,87.9,1.8206,24,666,20.2,68.95,34.02,8.4
441 | 9.39063,0,18.1,"0",0.74,5.627,93.9,1.8172,24,666,20.2,396.9,22.88,12.8
442 | 22.0511,0,18.1,"0",0.74,5.818,92.4,1.8662,24,666,20.2,391.45,22.11,10.5
443 | 9.72418,0,18.1,"0",0.74,6.406,97.2,2.0651,24,666,20.2,385.96,19.52,17.1
444 | 5.66637,0,18.1,"0",0.74,6.219,100,2.0048,24,666,20.2,395.69,16.59,18.4
445 | 9.96654,0,18.1,"0",0.74,6.485,100,1.9784,24,666,20.2,386.73,18.85,15.4
446 | 12.8023,0,18.1,"0",0.74,5.854,96.6,1.8956,24,666,20.2,240.52,23.79,10.8
447 | 10.6718,0,18.1,"0",0.74,6.459,94.8,1.9879,24,666,20.2,43.06,23.98,11.8
448 | 6.28807,0,18.1,"0",0.74,6.341,96.4,2.072,24,666,20.2,318.01,17.79,14.9
449 | 9.92485,0,18.1,"0",0.74,6.251,96.6,2.198,24,666,20.2,388.52,16.44,12.6
450 | 9.32909,0,18.1,"0",0.713,6.185,98.7,2.2616,24,666,20.2,396.9,18.13,14.1
451 | 7.52601,0,18.1,"0",0.713,6.417,98.3,2.185,24,666,20.2,304.21,19.31,13
452 | 6.71772,0,18.1,"0",0.713,6.749,92.6,2.3236,24,666,20.2,0.32,17.44,13.4
453 | 5.44114,0,18.1,"0",0.713,6.655,98.2,2.3552,24,666,20.2,355.29,17.73,15.2
454 | 5.09017,0,18.1,"0",0.713,6.297,91.8,2.3682,24,666,20.2,385.09,17.27,16.1
455 | 8.24809,0,18.1,"0",0.713,7.393,99.3,2.4527,24,666,20.2,375.87,16.74,17.8
456 | 9.51363,0,18.1,"0",0.713,6.728,94.1,2.4961,24,666,20.2,6.68,18.71,14.9
457 | 4.75237,0,18.1,"0",0.713,6.525,86.5,2.4358,24,666,20.2,50.92,18.13,14.1
458 | 4.66883,0,18.1,"0",0.713,5.976,87.9,2.5806,24,666,20.2,10.48,19.01,12.7
459 | 8.20058,0,18.1,"0",0.713,5.936,80.3,2.7792,24,666,20.2,3.5,16.94,13.5
460 | 7.75223,0,18.1,"0",0.713,6.301,83.7,2.7831,24,666,20.2,272.21,16.23,14.9
461 | 6.80117,0,18.1,"0",0.713,6.081,84.4,2.7175,24,666,20.2,396.9,14.7,20
462 | 4.81213,0,18.1,"0",0.713,6.701,90,2.5975,24,666,20.2,255.23,16.42,16.4
463 | 3.69311,0,18.1,"0",0.713,6.376,88.4,2.5671,24,666,20.2,391.43,14.65,17.7
464 | 6.65492,0,18.1,"0",0.713,6.317,83,2.7344,24,666,20.2,396.9,13.99,19.5
465 | 5.82115,0,18.1,"0",0.713,6.513,89.9,2.8016,24,666,20.2,393.82,10.29,20.2
466 | 7.83932,0,18.1,"0",0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22,21.4
467 | 3.1636,0,18.1,"0",0.655,5.759,48.2,3.0665,24,666,20.2,334.4,14.13,19.9
468 | 3.77498,0,18.1,"0",0.655,5.952,84.7,2.8715,24,666,20.2,22.01,17.15,19
469 | 4.42228,0,18.1,"0",0.584,6.003,94.5,2.5403,24,666,20.2,331.29,21.32,19.1
470 | 15.5757,0,18.1,"0",0.58,5.926,71,2.9084,24,666,20.2,368.74,18.13,19.1
471 | 13.0751,0,18.1,"0",0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76,20.1
472 | 4.34879,0,18.1,"0",0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29,19.9
473 | 4.03841,0,18.1,"0",0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87,19.6
474 | 3.56868,0,18.1,"0",0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36,23.2
475 | 4.64689,0,18.1,"0",0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66,29.8
476 | 8.05579,0,18.1,"0",0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14,13.8
477 | 6.39312,0,18.1,"0",0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1,13.3
478 | 4.87141,0,18.1,"0",0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68,16.7
479 | 15.0234,0,18.1,"0",0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91,12
480 | 10.233,0,18.1,"0",0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03,14.6
481 | 14.3337,0,18.1,"0",0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11,21.4
482 | 5.82401,0,18.1,"0",0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74,23
483 | 5.70818,0,18.1,"0",0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7
484 | 5.73116,0,18.1,"0",0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01,25
485 | 2.81838,0,18.1,"0",0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42,21.8
486 | 2.37857,0,18.1,"0",0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34,20.6
487 | 3.67367,0,18.1,"0",0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58,21.2
488 | 5.69175,0,18.1,"0",0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98,19.1
489 | 4.83567,0,18.1,"0",0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45,20.6
490 | 0.15086,0,27.74,"0",0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06,15.2
491 | 0.18337,0,27.74,"0",0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97,7
492 | 0.20746,0,27.74,"0",0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68,8.1
493 | 0.10574,0,27.74,"0",0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07,13.6
494 | 0.11132,0,27.74,"0",0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35,20.1
495 | 0.17331,0,9.69,"0",0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01,21.8
496 | 0.27957,0,9.69,"0",0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59,24.5
497 | 0.17899,0,9.69,"0",0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1
498 | 0.2896,0,9.69,"0",0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14,19.7
499 | 0.26838,0,9.69,"0",0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1,18.3
500 | 0.23912,0,9.69,"0",0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2
501 | 0.17783,0,9.69,"0",0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5
502 | 0.22438,0,9.69,"0",0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8
503 | 0.06263,0,11.93,"0",0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67,22.4
504 | 0.04527,0,11.93,"0",0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08,20.6
505 | 0.06076,0,11.93,"0",0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9
506 | 0.10959,0,11.93,"0",0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48,22
507 | 0.04741,0,11.93,"0",0.573,6.03,80.8,2.505,1,273,21,396.9,7.88,11.9
508 | 


--------------------------------------------------------------------------------
/Assignment_9.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "55e4241b",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import seaborn as sns\n",
 11 |     "titanic = sns.load_dataset(\"titanic\")"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 2,
 17 |    "id": "89318fdb",
 18 |    "metadata": {},
 19 |    "outputs": [
 20 |     {
 21 |      "data": {
 22 |       "text/html": [
 23 |        "<div>\n",
 24 |        "<style scoped>\n",
 25 |        "    .dataframe tbody tr th:only-of-type {\n",
 26 |        "        vertical-align: middle;\n",
 27 |        "    }\n",
 28 |        "\n",
 29 |        "    .dataframe tbody tr th {\n",
 30 |        "        vertical-align: top;\n",
 31 |        "    }\n",
 32 |        "\n",
 33 |        "    .dataframe thead th {\n",
 34 |        "        text-align: right;\n",
 35 |        "    }\n",
 36 |        "</style>\n",
 37 |        "<table border=\"1\" class=\"dataframe\">\n",
 38 |        "  <thead>\n",
 39 |        "    <tr style=\"text-align: right;\">\n",
 40 |        "      <th></th>\n",
 41 |        "      <th>survived</th>\n",
 42 |        "      <th>pclass</th>\n",
 43 |        "      <th>sex</th>\n",
 44 |        "      <th>age</th>\n",
 45 |        "      <th>sibsp</th>\n",
 46 |        "      <th>parch</th>\n",
 47 |        "      <th>fare</th>\n",
 48 |        "      <th>embarked</th>\n",
 49 |        "      <th>class</th>\n",
 50 |        "      <th>who</th>\n",
 51 |        "      <th>adult_male</th>\n",
 52 |        "      <th>deck</th>\n",
 53 |        "      <th>embark_town</th>\n",
 54 |        "      <th>alive</th>\n",
 55 |        "      <th>alone</th>\n",
 56 |        "    </tr>\n",
 57 |        "  </thead>\n",
 58 |        "  <tbody>\n",
 59 |        "    <tr>\n",
 60 |        "      <th>0</th>\n",
 61 |        "      <td>0</td>\n",
 62 |        "      <td>3</td>\n",
 63 |        "      <td>male</td>\n",
 64 |        "      <td>22.0</td>\n",
 65 |        "      <td>1</td>\n",
 66 |        "      <td>0</td>\n",
 67 |        "      <td>7.2500</td>\n",
 68 |        "      <td>S</td>\n",
 69 |        "      <td>Third</td>\n",
 70 |        "      <td>man</td>\n",
 71 |        "      <td>True</td>\n",
 72 |        "      <td>NaN</td>\n",
 73 |        "      <td>Southampton</td>\n",
 74 |        "      <td>no</td>\n",
 75 |        "      <td>False</td>\n",
 76 |        "    </tr>\n",
 77 |        "    <tr>\n",
 78 |        "      <th>1</th>\n",
 79 |        "      <td>1</td>\n",
 80 |        "      <td>1</td>\n",
 81 |        "      <td>female</td>\n",
 82 |        "      <td>38.0</td>\n",
 83 |        "      <td>1</td>\n",
 84 |        "      <td>0</td>\n",
 85 |        "      <td>71.2833</td>\n",
 86 |        "      <td>C</td>\n",
 87 |        "      <td>First</td>\n",
 88 |        "      <td>woman</td>\n",
 89 |        "      <td>False</td>\n",
 90 |        "      <td>C</td>\n",
 91 |        "      <td>Cherbourg</td>\n",
 92 |        "      <td>yes</td>\n",
 93 |        "      <td>False</td>\n",
 94 |        "    </tr>\n",
 95 |        "    <tr>\n",
 96 |        "      <th>2</th>\n",
 97 |        "      <td>1</td>\n",
 98 |        "      <td>3</td>\n",
 99 |        "      <td>female</td>\n",
100 |        "      <td>26.0</td>\n",
101 |        "      <td>0</td>\n",
102 |        "      <td>0</td>\n",
103 |        "      <td>7.9250</td>\n",
104 |        "      <td>S</td>\n",
105 |        "      <td>Third</td>\n",
106 |        "      <td>woman</td>\n",
107 |        "      <td>False</td>\n",
108 |        "      <td>NaN</td>\n",
109 |        "      <td>Southampton</td>\n",
110 |        "      <td>yes</td>\n",
111 |        "      <td>True</td>\n",
112 |        "    </tr>\n",
113 |        "    <tr>\n",
114 |        "      <th>3</th>\n",
115 |        "      <td>1</td>\n",
116 |        "      <td>1</td>\n",
117 |        "      <td>female</td>\n",
118 |        "      <td>35.0</td>\n",
119 |        "      <td>1</td>\n",
120 |        "      <td>0</td>\n",
121 |        "      <td>53.1000</td>\n",
122 |        "      <td>S</td>\n",
123 |        "      <td>First</td>\n",
124 |        "      <td>woman</td>\n",
125 |        "      <td>False</td>\n",
126 |        "      <td>C</td>\n",
127 |        "      <td>Southampton</td>\n",
128 |        "      <td>yes</td>\n",
129 |        "      <td>False</td>\n",
130 |        "    </tr>\n",
131 |        "    <tr>\n",
132 |        "      <th>4</th>\n",
133 |        "      <td>0</td>\n",
134 |        "      <td>3</td>\n",
135 |        "      <td>male</td>\n",
136 |        "      <td>35.0</td>\n",
137 |        "      <td>0</td>\n",
138 |        "      <td>0</td>\n",
139 |        "      <td>8.0500</td>\n",
140 |        "      <td>S</td>\n",
141 |        "      <td>Third</td>\n",
142 |        "      <td>man</td>\n",
143 |        "      <td>True</td>\n",
144 |        "      <td>NaN</td>\n",
145 |        "      <td>Southampton</td>\n",
146 |        "      <td>no</td>\n",
147 |        "      <td>True</td>\n",
148 |        "    </tr>\n",
149 |        "    <tr>\n",
150 |        "      <th>...</th>\n",
151 |        "      <td>...</td>\n",
152 |        "      <td>...</td>\n",
153 |        "      <td>...</td>\n",
154 |        "      <td>...</td>\n",
155 |        "      <td>...</td>\n",
156 |        "      <td>...</td>\n",
157 |        "      <td>...</td>\n",
158 |        "      <td>...</td>\n",
159 |        "      <td>...</td>\n",
160 |        "      <td>...</td>\n",
161 |        "      <td>...</td>\n",
162 |        "      <td>...</td>\n",
163 |        "      <td>...</td>\n",
164 |        "      <td>...</td>\n",
165 |        "      <td>...</td>\n",
166 |        "    </tr>\n",
167 |        "    <tr>\n",
168 |        "      <th>886</th>\n",
169 |        "      <td>0</td>\n",
170 |        "      <td>2</td>\n",
171 |        "      <td>male</td>\n",
172 |        "      <td>27.0</td>\n",
173 |        "      <td>0</td>\n",
174 |        "      <td>0</td>\n",
175 |        "      <td>13.0000</td>\n",
176 |        "      <td>S</td>\n",
177 |        "      <td>Second</td>\n",
178 |        "      <td>man</td>\n",
179 |        "      <td>True</td>\n",
180 |        "      <td>NaN</td>\n",
181 |        "      <td>Southampton</td>\n",
182 |        "      <td>no</td>\n",
183 |        "      <td>True</td>\n",
184 |        "    </tr>\n",
185 |        "    <tr>\n",
186 |        "      <th>887</th>\n",
187 |        "      <td>1</td>\n",
188 |        "      <td>1</td>\n",
189 |        "      <td>female</td>\n",
190 |        "      <td>19.0</td>\n",
191 |        "      <td>0</td>\n",
192 |        "      <td>0</td>\n",
193 |        "      <td>30.0000</td>\n",
194 |        "      <td>S</td>\n",
195 |        "      <td>First</td>\n",
196 |        "      <td>woman</td>\n",
197 |        "      <td>False</td>\n",
198 |        "      <td>B</td>\n",
199 |        "      <td>Southampton</td>\n",
200 |        "      <td>yes</td>\n",
201 |        "      <td>True</td>\n",
202 |        "    </tr>\n",
203 |        "    <tr>\n",
204 |        "      <th>888</th>\n",
205 |        "      <td>0</td>\n",
206 |        "      <td>3</td>\n",
207 |        "      <td>female</td>\n",
208 |        "      <td>NaN</td>\n",
209 |        "      <td>1</td>\n",
210 |        "      <td>2</td>\n",
211 |        "      <td>23.4500</td>\n",
212 |        "      <td>S</td>\n",
213 |        "      <td>Third</td>\n",
214 |        "      <td>woman</td>\n",
215 |        "      <td>False</td>\n",
216 |        "      <td>NaN</td>\n",
217 |        "      <td>Southampton</td>\n",
218 |        "      <td>no</td>\n",
219 |        "      <td>False</td>\n",
220 |        "    </tr>\n",
221 |        "    <tr>\n",
222 |        "      <th>889</th>\n",
223 |        "      <td>1</td>\n",
224 |        "      <td>1</td>\n",
225 |        "      <td>male</td>\n",
226 |        "      <td>26.0</td>\n",
227 |        "      <td>0</td>\n",
228 |        "      <td>0</td>\n",
229 |        "      <td>30.0000</td>\n",
230 |        "      <td>C</td>\n",
231 |        "      <td>First</td>\n",
232 |        "      <td>man</td>\n",
233 |        "      <td>True</td>\n",
234 |        "      <td>C</td>\n",
235 |        "      <td>Cherbourg</td>\n",
236 |        "      <td>yes</td>\n",
237 |        "      <td>True</td>\n",
238 |        "    </tr>\n",
239 |        "    <tr>\n",
240 |        "      <th>890</th>\n",
241 |        "      <td>0</td>\n",
242 |        "      <td>3</td>\n",
243 |        "      <td>male</td>\n",
244 |        "      <td>32.0</td>\n",
245 |        "      <td>0</td>\n",
246 |        "      <td>0</td>\n",
247 |        "      <td>7.7500</td>\n",
248 |        "      <td>Q</td>\n",
249 |        "      <td>Third</td>\n",
250 |        "      <td>man</td>\n",
251 |        "      <td>True</td>\n",
252 |        "      <td>NaN</td>\n",
253 |        "      <td>Queenstown</td>\n",
254 |        "      <td>no</td>\n",
255 |        "      <td>True</td>\n",
256 |        "    </tr>\n",
257 |        "  </tbody>\n",
258 |        "</table>\n",
259 |        "<p>891 rows × 15 columns</p>\n",
260 |        "</div>"
261 |       ],
262 |       "text/plain": [
263 |        "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
264 |        "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
265 |        "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
266 |        "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
267 |        "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
268 |        "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
269 |        "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
270 |        "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
271 |        "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
272 |        "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
273 |        "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
274 |        "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
275 |        "\n",
276 |        "       who  adult_male deck  embark_town alive  alone  \n",
277 |        "0      man        True  NaN  Southampton    no  False  \n",
278 |        "1    woman       False    C    Cherbourg   yes  False  \n",
279 |        "2    woman       False  NaN  Southampton   yes   True  \n",
280 |        "3    woman       False    C  Southampton   yes  False  \n",
281 |        "4      man        True  NaN  Southampton    no   True  \n",
282 |        "..     ...         ...  ...          ...   ...    ...  \n",
283 |        "886    man        True  NaN  Southampton    no   True  \n",
284 |        "887  woman       False    B  Southampton   yes   True  \n",
285 |        "888  woman       False  NaN  Southampton    no  False  \n",
286 |        "889    man        True    C    Cherbourg   yes   True  \n",
287 |        "890    man        True  NaN   Queenstown    no   True  \n",
288 |        "\n",
289 |        "[891 rows x 15 columns]"
290 |       ]
291 |      },
292 |      "execution_count": 2,
293 |      "metadata": {},
294 |      "output_type": "execute_result"
295 |     }
296 |    ],
297 |    "source": [
298 |     "titanic\n"
299 |    ]
300 |   },
301 |   {
302 |    "cell_type": "code",
303 |    "execution_count": 3,
304 |    "id": "d10760ef",
305 |    "metadata": {},
306 |    "outputs": [
307 |     {
308 |      "data": {
309 |       "text/html": [
310 |        "<div>\n",
311 |        "<style scoped>\n",
312 |        "    .dataframe tbody tr th:only-of-type {\n",
313 |        "        vertical-align: middle;\n",
314 |        "    }\n",
315 |        "\n",
316 |        "    .dataframe tbody tr th {\n",
317 |        "        vertical-align: top;\n",
318 |        "    }\n",
319 |        "\n",
320 |        "    .dataframe thead th {\n",
321 |        "        text-align: right;\n",
322 |        "    }\n",
323 |        "</style>\n",
324 |        "<table border=\"1\" class=\"dataframe\">\n",
325 |        "  <thead>\n",
326 |        "    <tr style=\"text-align: right;\">\n",
327 |        "      <th></th>\n",
328 |        "      <th>survived</th>\n",
329 |        "      <th>pclass</th>\n",
330 |        "      <th>sex</th>\n",
331 |        "      <th>age</th>\n",
332 |        "      <th>sibsp</th>\n",
333 |        "      <th>parch</th>\n",
334 |        "      <th>fare</th>\n",
335 |        "      <th>embarked</th>\n",
336 |        "      <th>class</th>\n",
337 |        "      <th>who</th>\n",
338 |        "      <th>adult_male</th>\n",
339 |        "      <th>deck</th>\n",
340 |        "      <th>embark_town</th>\n",
341 |        "      <th>alive</th>\n",
342 |        "      <th>alone</th>\n",
343 |        "    </tr>\n",
344 |        "  </thead>\n",
345 |        "  <tbody>\n",
346 |        "    <tr>\n",
347 |        "      <th>0</th>\n",
348 |        "      <td>0</td>\n",
349 |        "      <td>3</td>\n",
350 |        "      <td>male</td>\n",
351 |        "      <td>22.0</td>\n",
352 |        "      <td>1</td>\n",
353 |        "      <td>0</td>\n",
354 |        "      <td>7.2500</td>\n",
355 |        "      <td>S</td>\n",
356 |        "      <td>Third</td>\n",
357 |        "      <td>man</td>\n",
358 |        "      <td>True</td>\n",
359 |        "      <td>NaN</td>\n",
360 |        "      <td>Southampton</td>\n",
361 |        "      <td>no</td>\n",
362 |        "      <td>False</td>\n",
363 |        "    </tr>\n",
364 |        "    <tr>\n",
365 |        "      <th>1</th>\n",
366 |        "      <td>1</td>\n",
367 |        "      <td>1</td>\n",
368 |        "      <td>female</td>\n",
369 |        "      <td>38.0</td>\n",
370 |        "      <td>1</td>\n",
371 |        "      <td>0</td>\n",
372 |        "      <td>71.2833</td>\n",
373 |        "      <td>C</td>\n",
374 |        "      <td>First</td>\n",
375 |        "      <td>woman</td>\n",
376 |        "      <td>False</td>\n",
377 |        "      <td>C</td>\n",
378 |        "      <td>Cherbourg</td>\n",
379 |        "      <td>yes</td>\n",
380 |        "      <td>False</td>\n",
381 |        "    </tr>\n",
382 |        "    <tr>\n",
383 |        "      <th>2</th>\n",
384 |        "      <td>1</td>\n",
385 |        "      <td>3</td>\n",
386 |        "      <td>female</td>\n",
387 |        "      <td>26.0</td>\n",
388 |        "      <td>0</td>\n",
389 |        "      <td>0</td>\n",
390 |        "      <td>7.9250</td>\n",
391 |        "      <td>S</td>\n",
392 |        "      <td>Third</td>\n",
393 |        "      <td>woman</td>\n",
394 |        "      <td>False</td>\n",
395 |        "      <td>NaN</td>\n",
396 |        "      <td>Southampton</td>\n",
397 |        "      <td>yes</td>\n",
398 |        "      <td>True</td>\n",
399 |        "    </tr>\n",
400 |        "    <tr>\n",
401 |        "      <th>3</th>\n",
402 |        "      <td>1</td>\n",
403 |        "      <td>1</td>\n",
404 |        "      <td>female</td>\n",
405 |        "      <td>35.0</td>\n",
406 |        "      <td>1</td>\n",
407 |        "      <td>0</td>\n",
408 |        "      <td>53.1000</td>\n",
409 |        "      <td>S</td>\n",
410 |        "      <td>First</td>\n",
411 |        "      <td>woman</td>\n",
412 |        "      <td>False</td>\n",
413 |        "      <td>C</td>\n",
414 |        "      <td>Southampton</td>\n",
415 |        "      <td>yes</td>\n",
416 |        "      <td>False</td>\n",
417 |        "    </tr>\n",
418 |        "    <tr>\n",
419 |        "      <th>4</th>\n",
420 |        "      <td>0</td>\n",
421 |        "      <td>3</td>\n",
422 |        "      <td>male</td>\n",
423 |        "      <td>35.0</td>\n",
424 |        "      <td>0</td>\n",
425 |        "      <td>0</td>\n",
426 |        "      <td>8.0500</td>\n",
427 |        "      <td>S</td>\n",
428 |        "      <td>Third</td>\n",
429 |        "      <td>man</td>\n",
430 |        "      <td>True</td>\n",
431 |        "      <td>NaN</td>\n",
432 |        "      <td>Southampton</td>\n",
433 |        "      <td>no</td>\n",
434 |        "      <td>True</td>\n",
435 |        "    </tr>\n",
436 |        "    <tr>\n",
437 |        "      <th>5</th>\n",
438 |        "      <td>0</td>\n",
439 |        "      <td>3</td>\n",
440 |        "      <td>male</td>\n",
441 |        "      <td>NaN</td>\n",
442 |        "      <td>0</td>\n",
443 |        "      <td>0</td>\n",
444 |        "      <td>8.4583</td>\n",
445 |        "      <td>Q</td>\n",
446 |        "      <td>Third</td>\n",
447 |        "      <td>man</td>\n",
448 |        "      <td>True</td>\n",
449 |        "      <td>NaN</td>\n",
450 |        "      <td>Queenstown</td>\n",
451 |        "      <td>no</td>\n",
452 |        "      <td>True</td>\n",
453 |        "    </tr>\n",
454 |        "    <tr>\n",
455 |        "      <th>6</th>\n",
456 |        "      <td>0</td>\n",
457 |        "      <td>1</td>\n",
458 |        "      <td>male</td>\n",
459 |        "      <td>54.0</td>\n",
460 |        "      <td>0</td>\n",
461 |        "      <td>0</td>\n",
462 |        "      <td>51.8625</td>\n",
463 |        "      <td>S</td>\n",
464 |        "      <td>First</td>\n",
465 |        "      <td>man</td>\n",
466 |        "      <td>True</td>\n",
467 |        "      <td>E</td>\n",
468 |        "      <td>Southampton</td>\n",
469 |        "      <td>no</td>\n",
470 |        "      <td>True</td>\n",
471 |        "    </tr>\n",
472 |        "    <tr>\n",
473 |        "      <th>7</th>\n",
474 |        "      <td>0</td>\n",
475 |        "      <td>3</td>\n",
476 |        "      <td>male</td>\n",
477 |        "      <td>2.0</td>\n",
478 |        "      <td>3</td>\n",
479 |        "      <td>1</td>\n",
480 |        "      <td>21.0750</td>\n",
481 |        "      <td>S</td>\n",
482 |        "      <td>Third</td>\n",
483 |        "      <td>child</td>\n",
484 |        "      <td>False</td>\n",
485 |        "      <td>NaN</td>\n",
486 |        "      <td>Southampton</td>\n",
487 |        "      <td>no</td>\n",
488 |        "      <td>False</td>\n",
489 |        "    </tr>\n",
490 |        "    <tr>\n",
491 |        "      <th>8</th>\n",
492 |        "      <td>1</td>\n",
493 |        "      <td>3</td>\n",
494 |        "      <td>female</td>\n",
495 |        "      <td>27.0</td>\n",
496 |        "      <td>0</td>\n",
497 |        "      <td>2</td>\n",
498 |        "      <td>11.1333</td>\n",
499 |        "      <td>S</td>\n",
500 |        "      <td>Third</td>\n",
501 |        "      <td>woman</td>\n",
502 |        "      <td>False</td>\n",
503 |        "      <td>NaN</td>\n",
504 |        "      <td>Southampton</td>\n",
505 |        "      <td>yes</td>\n",
506 |        "      <td>False</td>\n",
507 |        "    </tr>\n",
508 |        "    <tr>\n",
509 |        "      <th>9</th>\n",
510 |        "      <td>1</td>\n",
511 |        "      <td>2</td>\n",
512 |        "      <td>female</td>\n",
513 |        "      <td>14.0</td>\n",
514 |        "      <td>1</td>\n",
515 |        "      <td>0</td>\n",
516 |        "      <td>30.0708</td>\n",
517 |        "      <td>C</td>\n",
518 |        "      <td>Second</td>\n",
519 |        "      <td>child</td>\n",
520 |        "      <td>False</td>\n",
521 |        "      <td>NaN</td>\n",
522 |        "      <td>Cherbourg</td>\n",
523 |        "      <td>yes</td>\n",
524 |        "      <td>False</td>\n",
525 |        "    </tr>\n",
526 |        "  </tbody>\n",
527 |        "</table>\n",
528 |        "</div>"
529 |       ],
530 |       "text/plain": [
531 |        "   survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
532 |        "0         0       3    male  22.0      1      0   7.2500        S   Third   \n",
533 |        "1         1       1  female  38.0      1      0  71.2833        C   First   \n",
534 |        "2         1       3  female  26.0      0      0   7.9250        S   Third   \n",
535 |        "3         1       1  female  35.0      1      0  53.1000        S   First   \n",
536 |        "4         0       3    male  35.0      0      0   8.0500        S   Third   \n",
537 |        "5         0       3    male   NaN      0      0   8.4583        Q   Third   \n",
538 |        "6         0       1    male  54.0      0      0  51.8625        S   First   \n",
539 |        "7         0       3    male   2.0      3      1  21.0750        S   Third   \n",
540 |        "8         1       3  female  27.0      0      2  11.1333        S   Third   \n",
541 |        "9         1       2  female  14.0      1      0  30.0708        C  Second   \n",
542 |        "\n",
543 |        "     who  adult_male deck  embark_town alive  alone  \n",
544 |        "0    man        True  NaN  Southampton    no  False  \n",
545 |        "1  woman       False    C    Cherbourg   yes  False  \n",
546 |        "2  woman       False  NaN  Southampton   yes   True  \n",
547 |        "3  woman       False    C  Southampton   yes  False  \n",
548 |        "4    man        True  NaN  Southampton    no   True  \n",
549 |        "5    man        True  NaN   Queenstown    no   True  \n",
550 |        "6    man        True    E  Southampton    no   True  \n",
551 |        "7  child       False  NaN  Southampton    no  False  \n",
552 |        "8  woman       False  NaN  Southampton   yes  False  \n",
553 |        "9  child       False  NaN    Cherbourg   yes  False  "
554 |       ]
555 |      },
556 |      "execution_count": 3,
557 |      "metadata": {},
558 |      "output_type": "execute_result"
559 |     }
560 |    ],
561 |    "source": [
562 |     "titanic.head(10)"
563 |    ]
564 |   },
565 |   {
566 |    "cell_type": "code",
567 |    "execution_count": 4,
568 |    "id": "c279ecfd",
569 |    "metadata": {},
570 |    "outputs": [
571 |     {
572 |      "data": {
573 |       "text/plain": [
574 |        "<bound method DataFrame.info of      survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
575 |        "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
576 |        "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
577 |        "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
578 |        "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
579 |        "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
580 |        "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
581 |        "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
582 |        "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
583 |        "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
584 |        "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
585 |        "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
586 |        "\n",
587 |        "       who  adult_male deck  embark_town alive  alone  \n",
588 |        "0      man        True  NaN  Southampton    no  False  \n",
589 |        "1    woman       False    C    Cherbourg   yes  False  \n",
590 |        "2    woman       False  NaN  Southampton   yes   True  \n",
591 |        "3    woman       False    C  Southampton   yes  False  \n",
592 |        "4      man        True  NaN  Southampton    no   True  \n",
593 |        "..     ...         ...  ...          ...   ...    ...  \n",
594 |        "886    man        True  NaN  Southampton    no   True  \n",
595 |        "887  woman       False    B  Southampton   yes   True  \n",
596 |        "888  woman       False  NaN  Southampton    no  False  \n",
597 |        "889    man        True    C    Cherbourg   yes   True  \n",
598 |        "890    man        True  NaN   Queenstown    no   True  \n",
599 |        "\n",
600 |        "[891 rows x 15 columns]>"
601 |       ]
602 |      },
603 |      "execution_count": 4,
604 |      "metadata": {},
605 |      "output_type": "execute_result"
606 |     }
607 |    ],
608 |    "source": [
609 |     "titanic.info"
610 |    ]
611 |   },
612 |   {
613 |    "cell_type": "code",
614 |    "execution_count": 5,
615 |    "id": "b7d3c636",
616 |    "metadata": {},
617 |    "outputs": [
618 |     {
619 |      "data": {
620 |       "text/html": [
621 |        "<div>\n",
622 |        "<style scoped>\n",
623 |        "    .dataframe tbody tr th:only-of-type {\n",
624 |        "        vertical-align: middle;\n",
625 |        "    }\n",
626 |        "\n",
627 |        "    .dataframe tbody tr th {\n",
628 |        "        vertical-align: top;\n",
629 |        "    }\n",
630 |        "\n",
631 |        "    .dataframe thead th {\n",
632 |        "        text-align: right;\n",
633 |        "    }\n",
634 |        "</style>\n",
635 |        "<table border=\"1\" class=\"dataframe\">\n",
636 |        "  <thead>\n",
637 |        "    <tr style=\"text-align: right;\">\n",
638 |        "      <th></th>\n",
639 |        "      <th>survived</th>\n",
640 |        "      <th>pclass</th>\n",
641 |        "      <th>age</th>\n",
642 |        "      <th>sibsp</th>\n",
643 |        "      <th>parch</th>\n",
644 |        "      <th>fare</th>\n",
645 |        "    </tr>\n",
646 |        "  </thead>\n",
647 |        "  <tbody>\n",
648 |        "    <tr>\n",
649 |        "      <th>count</th>\n",
650 |        "      <td>891.000000</td>\n",
651 |        "      <td>891.000000</td>\n",
652 |        "      <td>714.000000</td>\n",
653 |        "      <td>891.000000</td>\n",
654 |        "      <td>891.000000</td>\n",
655 |        "      <td>891.000000</td>\n",
656 |        "    </tr>\n",
657 |        "    <tr>\n",
658 |        "      <th>mean</th>\n",
659 |        "      <td>0.383838</td>\n",
660 |        "      <td>2.308642</td>\n",
661 |        "      <td>29.699118</td>\n",
662 |        "      <td>0.523008</td>\n",
663 |        "      <td>0.381594</td>\n",
664 |        "      <td>32.204208</td>\n",
665 |        "    </tr>\n",
666 |        "    <tr>\n",
667 |        "      <th>std</th>\n",
668 |        "      <td>0.486592</td>\n",
669 |        "      <td>0.836071</td>\n",
670 |        "      <td>14.526497</td>\n",
671 |        "      <td>1.102743</td>\n",
672 |        "      <td>0.806057</td>\n",
673 |        "      <td>49.693429</td>\n",
674 |        "    </tr>\n",
675 |        "    <tr>\n",
676 |        "      <th>min</th>\n",
677 |        "      <td>0.000000</td>\n",
678 |        "      <td>1.000000</td>\n",
679 |        "      <td>0.420000</td>\n",
680 |        "      <td>0.000000</td>\n",
681 |        "      <td>0.000000</td>\n",
682 |        "      <td>0.000000</td>\n",
683 |        "    </tr>\n",
684 |        "    <tr>\n",
685 |        "      <th>25%</th>\n",
686 |        "      <td>0.000000</td>\n",
687 |        "      <td>2.000000</td>\n",
688 |        "      <td>20.125000</td>\n",
689 |        "      <td>0.000000</td>\n",
690 |        "      <td>0.000000</td>\n",
691 |        "      <td>7.910400</td>\n",
692 |        "    </tr>\n",
693 |        "    <tr>\n",
694 |        "      <th>50%</th>\n",
695 |        "      <td>0.000000</td>\n",
696 |        "      <td>3.000000</td>\n",
697 |        "      <td>28.000000</td>\n",
698 |        "      <td>0.000000</td>\n",
699 |        "      <td>0.000000</td>\n",
700 |        "      <td>14.454200</td>\n",
701 |        "    </tr>\n",
702 |        "    <tr>\n",
703 |        "      <th>75%</th>\n",
704 |        "      <td>1.000000</td>\n",
705 |        "      <td>3.000000</td>\n",
706 |        "      <td>38.000000</td>\n",
707 |        "      <td>1.000000</td>\n",
708 |        "      <td>0.000000</td>\n",
709 |        "      <td>31.000000</td>\n",
710 |        "    </tr>\n",
711 |        "    <tr>\n",
712 |        "      <th>max</th>\n",
713 |        "      <td>1.000000</td>\n",
714 |        "      <td>3.000000</td>\n",
715 |        "      <td>80.000000</td>\n",
716 |        "      <td>8.000000</td>\n",
717 |        "      <td>6.000000</td>\n",
718 |        "      <td>512.329200</td>\n",
719 |        "    </tr>\n",
720 |        "  </tbody>\n",
721 |        "</table>\n",
722 |        "</div>"
723 |       ],
724 |       "text/plain": [
725 |        "         survived      pclass         age       sibsp       parch        fare\n",
726 |        "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
727 |        "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
728 |        "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
729 |        "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
730 |        "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
731 |        "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
732 |        "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
733 |        "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
734 |       ]
735 |      },
736 |      "execution_count": 5,
737 |      "metadata": {},
738 |      "output_type": "execute_result"
739 |     }
740 |    ],
741 |    "source": [
742 |     "titanic.describe()"
743 |    ]
744 |   },
745 |   {
746 |    "cell_type": "code",
747 |    "execution_count": 6,
748 |    "id": "beedac32",
749 |    "metadata": {},
750 |    "outputs": [
751 |     {
752 |      "data": {
753 |       "text/html": [
754 |        "<div>\n",
755 |        "<style scoped>\n",
756 |        "    .dataframe tbody tr th:only-of-type {\n",
757 |        "        vertical-align: middle;\n",
758 |        "    }\n",
759 |        "\n",
760 |        "    .dataframe tbody tr th {\n",
761 |        "        vertical-align: top;\n",
762 |        "    }\n",
763 |        "\n",
764 |        "    .dataframe thead th {\n",
765 |        "        text-align: right;\n",
766 |        "    }\n",
767 |        "</style>\n",
768 |        "<table border=\"1\" class=\"dataframe\">\n",
769 |        "  <thead>\n",
770 |        "    <tr style=\"text-align: right;\">\n",
771 |        "      <th></th>\n",
772 |        "      <th>survived</th>\n",
773 |        "      <th>alive</th>\n",
774 |        "    </tr>\n",
775 |        "  </thead>\n",
776 |        "  <tbody>\n",
777 |        "    <tr>\n",
778 |        "      <th>0</th>\n",
779 |        "      <td>0</td>\n",
780 |        "      <td>no</td>\n",
781 |        "    </tr>\n",
782 |        "    <tr>\n",
783 |        "      <th>1</th>\n",
784 |        "      <td>1</td>\n",
785 |        "      <td>yes</td>\n",
786 |        "    </tr>\n",
787 |        "    <tr>\n",
788 |        "      <th>2</th>\n",
789 |        "      <td>1</td>\n",
790 |        "      <td>yes</td>\n",
791 |        "    </tr>\n",
792 |        "    <tr>\n",
793 |        "      <th>3</th>\n",
794 |        "      <td>1</td>\n",
795 |        "      <td>yes</td>\n",
796 |        "    </tr>\n",
797 |        "    <tr>\n",
798 |        "      <th>4</th>\n",
799 |        "      <td>0</td>\n",
800 |        "      <td>no</td>\n",
801 |        "    </tr>\n",
802 |        "    <tr>\n",
803 |        "      <th>...</th>\n",
804 |        "      <td>...</td>\n",
805 |        "      <td>...</td>\n",
806 |        "    </tr>\n",
807 |        "    <tr>\n",
808 |        "      <th>886</th>\n",
809 |        "      <td>0</td>\n",
810 |        "      <td>no</td>\n",
811 |        "    </tr>\n",
812 |        "    <tr>\n",
813 |        "      <th>887</th>\n",
814 |        "      <td>1</td>\n",
815 |        "      <td>yes</td>\n",
816 |        "    </tr>\n",
817 |        "    <tr>\n",
818 |        "      <th>888</th>\n",
819 |        "      <td>0</td>\n",
820 |        "      <td>no</td>\n",
821 |        "    </tr>\n",
822 |        "    <tr>\n",
823 |        "      <th>889</th>\n",
824 |        "      <td>1</td>\n",
825 |        "      <td>yes</td>\n",
826 |        "    </tr>\n",
827 |        "    <tr>\n",
828 |        "      <th>890</th>\n",
829 |        "      <td>0</td>\n",
830 |        "      <td>no</td>\n",
831 |        "    </tr>\n",
832 |        "  </tbody>\n",
833 |        "</table>\n",
834 |        "<p>891 rows × 2 columns</p>\n",
835 |        "</div>"
836 |       ],
837 |       "text/plain": [
838 |        "     survived alive\n",
839 |        "0           0    no\n",
840 |        "1           1   yes\n",
841 |        "2           1   yes\n",
842 |        "3           1   yes\n",
843 |        "4           0    no\n",
844 |        "..        ...   ...\n",
845 |        "886         0    no\n",
846 |        "887         1   yes\n",
847 |        "888         0    no\n",
848 |        "889         1   yes\n",
849 |        "890         0    no\n",
850 |        "\n",
851 |        "[891 rows x 2 columns]"
852 |       ]
853 |      },
854 |      "execution_count": 6,
855 |      "metadata": {},
856 |      "output_type": "execute_result"
857 |     }
858 |    ],
859 |    "source": [
860 |     "titanic.loc[:,[\"survived\",\"alive\"]]"
861 |    ]
862 |   },
863 |   {
864 |    "cell_type": "code",
865 |    "execution_count": 7,
866 |    "id": "14ed98a3",
867 |    "metadata": {},
868 |    "outputs": [
869 |     {
870 |      "data": {
871 |       "text/plain": [
872 |        "<AxesSubplot:xlabel='sex', ylabel='age'>"
873 |       ]
874 |      },
875 |      "execution_count": 7,
876 |      "metadata": {},
877 |      "output_type": "execute_result"
878 |     },
879 |     {
880 |      "data": {
881 |       "image/png": 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DAIBg4niZGTRokDwej38555xzJB2dlSktLVVBQYFycnKUkpKiiooKHT58WJWVlQ6nBgAAwcLxMvPhhx8qLi5OSUlJ+v73v69du3ZJknbv3q3GxkZlZWX5t3W5XEpPT1dtbW2fr+fz+dTS0tJtAQAAocvRMpOamqqnnnpKGzZs0KpVq9TY2Ki0tDQdOHBAjY2NkiS3293tOW6327+uN0VFRYqNjfUvCQkJA7oPAADAWY6WmezsbH3ve9/T+PHjdd111+mFF16QJFVUVPi3sSyr23Ns2+4xdrxFixapubnZvzQ0NAxMeAAAEBQcP8x0vCFDhmj8+PH68MMP/Z9q+vIsTFNTU4/ZmuO5XC7FxMR0WwAAQOgKqjLj8/m0c+dOjRw5UklJSfJ4PKqurvav7+joUE1NjdLS0hxMCQAAgskgJ7/4T3/6U02dOlWjRo1SU1OTli5dqpaWFs2ePVuWZSk/P1+FhYVKTk5WcnKyCgsLFRUVpdzcXCdjAwCAIOJomdmzZ49mzJih/fv365xzztE//MM/6K233lJiYqIkaeHChWpra1NeXp4OHjyo1NRUVVVVKTo62snYAAAgiDhaZtasWXPC9ZZlyev1yuv1BiYQAAAwTlCdMwMAAPBNUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGO6ky89FHH2nDhg1qa2uTJNm2fUpCAQAAfF39KjMHDhzQddddp7Fjx+r666/X3r17JUk/+tGP9JOf/OSUBgQAADiRfpWZ+fPna9CgQaqvr1dUVJR/fPr06Vq/fv0pCwcAAPBVBvXnSVVVVdqwYYPi4+O7jScnJ+vTTz89JcEAAAC+jn7NzBw6dKjbjMwx+/fvl8vlOulQAAAAX1e/ysw111yjp556yv/Ysix1dXVp+fLl+va3v33KwgEAAHyVfh1mWr58uTIyMrRp0yZ1dHRo4cKF2r59u/7617/qjTfeONUZAQAA+tSvmZmLLrpIW7du1RVXXKHMzEwdOnRIOTk5evfdd3Xeeef1K0hRUZEsy1J+fr5/zLZteb1excXFKTIyUhkZGdq+fXu/Xh8AAISmfs3MSJLH49FDDz10SkLU1dWpvLxcEyZM6DZeXFyskpISPfnkkxo7dqyWLl2qzMxMvf/++4qOjj4lXxsAAJitX2Vm69atvY5blqWIiAiNGjXqa58I/MUXX2jmzJlatWqVli5d6h+3bVulpaUqKChQTk6OJKmiokJut1uVlZWaO3duf6IDAIAQ068yc8kll8iyLEl/v+rvsceSdMYZZ2j69Ol6/PHHFRERccLXuvPOO3XDDTfouuuu61Zmdu/ercbGRmVlZfnHXC6X0tPTVVtb22eZ8fl88vl8/sctLS3ffAcBAIAx+nXOzNq1a5WcnKzy8nL9+c9/1pYtW1ReXq5x48apsrJSTzzxhF5++WU98MADJ3ydNWvWaPPmzSoqKuqxrrGxUZLkdru7jbvdbv+63hQVFSk2Nta/JCQk9GMPAQCAKfo1M/Pwww9rxYoVmjJlin9swoQJio+P1+LFi7Vx40YNGTJEP/nJT/TLX/6y19doaGjQvHnzVFVVdcLZm+NnfKSjM0FfHjveokWLtGDBAv/jlpYWCg0AACGsX2Vm27ZtSkxM7DGemJiobdu2STp6KOrYPZt6s3nzZjU1Nemyyy7zj3V2duq1115TWVmZ3n//fUlHZ2hGjhzp36apqanHbM3xXC5XQC/cZ9u22tvbA/b10Lfjvw98T4JHRETECf8DAgAnq19l5oILLtAjjzyi8vJyDR48WJL0t7/9TY888oguuOACSdJf/vKXE5aOf/zHf/QXn2N++MMf6oILLtC9996rMWPGyOPxqLq6WhMnTpQkdXR0qKamRsuWLetP7AHR3t6u7Oxsp2PgS6ZNm+Z0BPx/69atU2RkpNMxAISwfpWZ3/zmN/rOd76j+Ph4TZgwQZZlaevWrers7NQf//hHSdKuXbuUl5fX52tER0crJSWl29iQIUM0bNgw/3h+fr4KCwuVnJys5ORkFRYWKioqSrm5uf2JDQAAQlC/ykxaWpo++eQTPf300/rggw9k27Zuvvlm5ebm+q//MmvWrJMOt3DhQrW1tSkvL08HDx5UamqqqqqqgvYaM19cMkN2WL8v3YOTZdtS15Gjfw4bJHFowzFW1xEN3fIfTscAcJro92/eoUOH6pprrtHo0aPV0dEhSXrllVckSd/5znf69Zqvvvpqt8eWZcnr9crr9fY3ZkDZYYOk8DOcjnGaG+x0AEiynQ4A4LTSrzKza9cuTZs2Tdu2bZNlWT0+YdTZ2XnKAgIAAJxIv64zM2/ePCUlJWnfvn2KiorSe++9p5qaGk2aNKnH7AoAAMBA6tfMzJtvvqmXX35Z55xzjsLCwhQeHq7JkyerqKhI99xzj959991TnRMAAKBX/SoznZ2dGjp0qCRp+PDh+uyzzzRu3DglJib6rw8DAKbjOlLBg+tIBadguY5Uv8pMSkqKtm7dqjFjxig1NVXFxcUaPHiwysvLNWbMmFOdEQAcwXWkghPXkQoewXIdqX6VmQceeECHDh2SJC1dulQ33nijrr76ag0bNkzPPvvsKQ0IAABwIv0qM8ffk2nMmDHasWOH/vrXv+qss84KiukmADjVyib/Va5wPnTuFNuWOrqO/nlwGJeRcpKv09Jdr5/tdIxuTtkV3s4+O7h2DABOJVe4LVe40ylOb33fkhiBFXylvl8fzQYAAAgWlBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjOVpmVq5cqQkTJigmJkYxMTG68sortW7dOv9627bl9XoVFxenyMhIZWRkaPv27Q4mBgAAwcbRMhMfH69HHnlEmzZt0qZNm3Tttdfqpptu8heW4uJilZSUqKysTHV1dfJ4PMrMzFRra6uTsQEAQBBxtMxMnTpV119/vcaOHauxY8fq4Ycf1tChQ/XWW2/Jtm2VlpaqoKBAOTk5SklJUUVFhQ4fPqzKykonYwMAgCASNOfMdHZ2as2aNTp06JCuvPJK7d69W42NjcrKyvJv43K5lJ6ertra2j5fx+fzqaWlpdsCAABCl+NlZtu2bRo6dKhcLpfuuOMOrV27VhdddJEaGxslSW63u9v2brfbv643RUVFio2N9S8JCQkDmh8AADjL8TIzbtw4bdmyRW+99ZZ+/OMfa/bs2dqxY4d/vWVZ3ba3bbvH2PEWLVqk5uZm/9LQ0DBg2QEAgPMGOR1g8ODBOv/88yVJkyZNUl1dnVasWKF7771XktTY2KiRI0f6t29qauoxW3M8l8sll8s1sKGPY9v23x90/i1gXxcIase9F7q9RwBgADheZr7Mtm35fD4lJSXJ4/GourpaEydOlCR1dHSopqZGy5Ytczjl3/l8Pv+fo/+8xsEkQHDy+XyKiopyOgaAEOZombn//vuVnZ2thIQEtba2as2aNXr11Ve1fv16WZal/Px8FRYWKjk5WcnJySosLFRUVJRyc3OdjA0AAIKIo2Vm3759mjVrlvbu3avY2FhNmDBB69evV2ZmpiRp4cKFamtrU15eng4ePKjU1FRVVVUpOjraydjdHH9Iq/Vb35fCz3AwDRAkOv/mn6kM5GFfAKcnR8vME088ccL1lmXJ6/XK6/UGJlA/dDsZOfwMygzwJSc6YR8ATgXHP80EAABwMigzAADAaJQZAABgNMoMAAAwWtBdZwYAgsXxF/zzdToYBAgix78XguWimJQZAOjD8RfFvOv1YQ4mAYJTsFwUk8NMAADAaMzMAEAfjr/gX9nkA3KFOxgGCBK+zr/PVAbLRTEpMwDQh+Mv+OcKF2UG+JJguSgmh5kAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAw2iCnA4QSq+uIbKdDnM5sW+o6cvTPYYMky3I2z2nMOvZ9AIAAoMycQkO3/IfTEQAAOO1wmAkAABjN0ZmZoqIi/e53v9P//u//KjIyUmlpaVq2bJnGjRvn38a2bT300EMqLy/XwYMHlZqaqt/85je6+OKLHUz+dxEREVq3bp3TMSCpvb1d06ZNkyStXbtWERERDieCJL4PAAaco2WmpqZGd955py6//HIdOXJEBQUFysrK0o4dOzRkyBBJUnFxsUpKSvTkk09q7NixWrp0qTIzM/X+++8rOjrayfiSJMuyFBkZ6XQMfElERATfFwA4TThaZtavX9/t8erVqzVixAht3rxZ11xzjWzbVmlpqQoKCpSTkyNJqqiokNvtVmVlpebOndvjNX0+n3w+n/9xS0vLwO4EAABwVFCdM9Pc3CxJOvvssyVJu3fvVmNjo7KysvzbuFwupaenq7a2ttfXKCoqUmxsrH9JSEgY+OAAAMAxQVNmbNvWggULNHnyZKWkpEiSGhsbJUlut7vbtm6327/uyxYtWqTm5mb/0tDQMLDBAQCAo4Lmo9l33XWXtm7dqtdff73HOutL1wuxbbvH2DEul0sul2tAMgIAgOATFDMzd999t/7whz/olVdeUXx8vH/c4/FIUo9ZmKamph6zNQAA4PTkaJmxbVt33XWXfve73+nll19WUlJSt/VJSUnyeDyqrq72j3V0dKimpkZpaWmBjgsAAIKQo4eZ7rzzTlVWVuq//uu/FB0d7Z+BiY2NVWRkpCzLUn5+vgoLC5WcnKzk5GQVFhYqKipKubm5TkYHAABBwtEys3LlSklSRkZGt/HVq1drzpw5kqSFCxeqra1NeXl5/ovmVVVVBcU1ZgAAgPMcLTO2/dW3ZbQsS16vV16vd+ADAUAffJ2WxK1kHWPbUkfX0T8PDuM+sk46+l4ILkHzaSYACGZ3vX620xEA9CEoPs0EAADQX8zMAEAfuJFs8OBGssEpWL4PlBkA6AM3kg1O3EgWX8ZhJgAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGM3RMvPaa69p6tSpiouLk2VZ+v3vf99tvW3b8nq9iouLU2RkpDIyMrR9+3ZnwgIAgKDkaJk5dOiQvvWtb6msrKzX9cXFxSopKVFZWZnq6urk8XiUmZmp1tbWACcFAADBapCTXzw7O1vZ2dm9rrNtW6WlpSooKFBOTo4kqaKiQm63W5WVlZo7d26vz/P5fPL5fP7HLS0tpz44AAAIGkF7zszu3bvV2NiorKws/5jL5VJ6erpqa2v7fF5RUZFiY2P9S0JCQiDiAgAAhwRtmWlsbJQkud3ubuNut9u/rjeLFi1Sc3Ozf2loaBjQnAAAwFmOHmb6OizL6vbYtu0eY8dzuVxyuVwDHQsAAASJoJ2Z8Xg8ktRjFqapqanHbA0AADh9BW2ZSUpKksfjUXV1tX+so6NDNTU1SktLczAZAAAIJo4eZvriiy/00Ucf+R/v3r1bW7Zs0dlnn61Ro0YpPz9fhYWFSk5OVnJysgoLCxUVFaXc3FwHUwMAgGDiaJnZtGmTvv3tb/sfL1iwQJI0e/ZsPfnkk1q4cKHa2tqUl5engwcPKjU1VVVVVYqOjnYqMgAACDKOlpmMjAzZtt3nesuy5PV65fV6AxcKAAAYJWjPmQEAAPg6KDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIw2yOkAAICBY9u22tvbnY5x0o7fh1DYH0mKiIiQZVlOxwgJRpSZxx57TMuXL9fevXt18cUXq7S0VFdffbXTsUIGP+yCFz/scLLa29uVnZ3tdIxTatq0aU5HOCXWrVunyMhIp2OEhKAvM88++6zy8/P12GOP6aqrrtLjjz+u7Oxs7dixQ6NGjXI6Xkjgh13w4ocdAHw1y7Zt2+kQJ5KamqpLL71UK1eu9I9deOGF+u53v6uioqKvfH5LS4tiY2PV3NysmJiYgYxqrLa2tpArM6GCMoOTFSozr7Zty+fzSZJcLldIzFgy83pi3+T3d1DPzHR0dGjz5s267777uo1nZWWptra21+f4fD7/P3jp6F8GTiwiIkLr1q1zOsZJC9UfdsDJsCwrZApxVFSU0xEQpIK6zOzfv1+dnZ1yu93dxt1utxobG3t9TlFRkR566KFAxAsZ/LADAJjMiI9mf/l/2LZt9/m/7kWLFqm5udm/NDQ0BCIiAABwSFDPzAwfPlzh4eE9ZmGampp6zNYc43K55HK5AhEPAAAEgaCemRk8eLAuu+wyVVdXdxuvrq5WWlqaQ6kAAEAwCeqZGUlasGCBZs2apUmTJunKK69UeXm56uvrdccddzgdDQAABIGgLzPTp0/XgQMHtGTJEu3du1cpKSn605/+pMTERKejAQCAIBD015k5WVxnBgAA83yT399Bfc4MAADAV6HMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYLegvmneyjl1Gp6WlxeEkAADg6zr2e/vrXA4v5MtMa2urJCkhIcHhJAAA4JtqbW1VbGzsCbcJ+SsAd3V16bPPPlN0dLQsy3I6DgZYS0uLEhIS1NDQwBWfgRDD+/v0Ytu2WltbFRcXp7CwE58VE/IzM2FhYYqPj3c6BgIsJiaGH3ZAiOL9ffr4qhmZYzgBGAAAGI0yAwAAjEaZQUhxuVz6+c9/LpfL5XQUAKcY72/0JeRPAAYAAKGNmRkAAGA0ygwAADAaZQYAABiNMoPTwpw5c/Td737X6RjAacG2bf3Lv/yLzj77bFmWpS1btjiS45NPPnH06yNwQv6ieQCAwFq/fr2efPJJvfrqqxozZoyGDx/udCSEOMoMAOCU+vjjjzVy5EilpaU5HQWnCQ4zIehkZGTo7rvvVn5+vs466yy53W6Vl5fr0KFD+uEPf6jo6Gidd955WrdunSSps7NTt99+u5KSkhQZGalx48ZpxYoVJ/watm2ruLhYY8aMUWRkpL71rW/p+eefD8TuASFtzpw5uvvuu1VfXy/LsjR69OivfL+9+uqrsixLGzZs0MSJExUZGalrr71WTU1NWrdunS688ELFxMRoxowZOnz4sP9569ev1+TJk3XmmWdq2LBhuvHGG/Xxxx+fMN+OHTt0/fXXa+jQoXK73Zo1a5b2798/YH8fCAzKDIJSRUWFhg8fro0bN+ruu+/Wj3/8Y91yyy1KS0vTO++8oylTpmjWrFk6fPiwurq6FB8fr+eee047duzQgw8+qPvvv1/PPfdcn6//wAMPaPXq1Vq5cqW2b9+u+fPn6wc/+IFqamoCuJdA6FmxYoWWLFmi+Ph47d27V3V1dV/7/eb1elVWVqba2lo1NDTo1ltvVWlpqSorK/XCCy+ourpajz76qH/7Q4cOacGCBaqrq9NLL72ksLAwTZs2TV1dXb1m27t3r9LT03XJJZdo06ZNWr9+vfbt26dbb711QP9OEAA2EGTS09PtyZMn+x8fOXLEHjJkiD1r1iz/2N69e21J9ptvvtnra+Tl5dnf+973/I9nz55t33TTTbZt2/YXX3xhR0RE2LW1td2ec/vtt9szZsw4hXsCnJ7+9V//1U5MTLRt++u931555RVbkv3iiy/61xcVFdmS7I8//tg/NnfuXHvKlCl9ft2mpiZbkr1t2zbbtm179+7dtiT73XfftW3bthcvXmxnZWV1e05DQ4MtyX7//ff7vb9wHufMIChNmDDB/+fw8HANGzZM48eP94+53W5JUlNTkyTpt7/9rf7t3/5Nn376qdra2tTR0aFLLrmk19fesWOH2tvblZmZ2W28o6NDEydOPMV7Apzevsn77fj3vdvtVlRUlMaMGdNtbOPGjf7HH3/8sRYvXqy33npL+/fv98/I1NfXKyUlpUeWzZs365VXXtHQoUN7rPv44481duzY/u0kHEeZQVA644wzuj22LKvbmGVZkqSuri4999xzmj9/vn71q1/pyiuvVHR0tJYvX663336719c+9gPvhRde0LnnntttHfd8AU6tb/J++/J7vLefA8cfQpo6daoSEhK0atUqxcXFqaurSykpKero6Ogzy9SpU7Vs2bIe60aOHPnNdgxBhTID4/3P//yP0tLSlJeX5x870UmAF110kVwul+rr65Wenh6IiMBpa6DebwcOHNDOnTv1+OOP6+qrr5Ykvf766yd8zqWXXqr//M//1OjRozVoEL/+QgnfTRjv/PPP11NPPaUNGzYoKSlJ//7v/666ujolJSX1un10dLR++tOfav78+erq6tLkyZPV0tKi2tpaDR06VLNnzw7wHgCha6Deb2eddZaGDRum8vJyjRw5UvX19brvvvtO+Jw777xTq1at0owZM/Szn/1Mw4cP10cffaQ1a9Zo1apVCg8P71cWOI8yA+Pdcccd2rJli6ZPny7LsjRjxgzl5eX5P7rdm1/84hcaMWKEioqKtGvXLp155pm69NJLdf/99wcwOXB6GIj3W1hYmNasWaN77rlHKSkpGjdunH79618rIyOjz+fExcXpjTfe0L333qspU6bI5/MpMTFR//RP/6SwMD7cazLLtm3b6RAAAAD9RRUFAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwCC0vPPP6/x48crMjJSw4YN03XXXadDhw5JklavXq0LL7xQERERuuCCC/TYY4/5n3fbbbdpwoQJ8vl8kqS//e1vuuyyyzRz5kxH9gPAwKPMAAg6e/fu1YwZM3Tbbbdp586devXVV5WTkyPbtrVq1SoVFBTo4Ycf1s6dO1VYWKjFixeroqJCkvTrX/9ahw4d0n333SdJWrx4sfbv39+t8AAILdw1G0DQeeedd3TZZZfpk08+UWJiYrd1o0aN0rJlyzRjxgz/2NKlS/WnP/1JtbW1kqQ333xT6enpuu+++1RUVKSXXnpJ11xzTUD3AUDgUGYABJ3Ozk5NmTJFGzdu1JQpU5SVlaWbb75ZR44c0YgRIxQZGamwsL9PLB85ckSxsbHat2+ff+z+++9XUVGR7r33Xj3yyCNO7AaAABnkdAAA+LLw8HBVV1ertrZWVVVVevTRR1VQUKD//u//liStWrVKqampPZ5zTFdXl9544w2Fh4frww8/DGh2AIHHOTMAgpJlWbrqqqv00EMP6d1339XgwYP1xhtv6Nxzz9WuXbt0/vnnd1uSkpL8z12+fLl27typmpoabdiwQatXr3ZwTwAMNGZmAASdt99+Wy+99JKysrI0YsQIvf322/q///s/XXjhhfJ6vbrnnnsUExOj7Oxs+Xw+bdq0SQcPHtSCBQu0ZcsWPfjgg3r++ed11VVXacWKFZo3b57S09M1ZswYp3cNwADgnBkAQWfnzp2aP3++3nnnHbW0tCgxMVF333237rrrLklSZWWlli9frh07dmjIkCEaP3688vPzlZ2drcsuu0yTJ0/W448/7n+9nJwc7du3T6+99lq3w1EAQgNlBgAAGI1zZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgtP8H1arcd3LPGTAAAAAASUVORK5CYII=\n",
882 |       "text/plain": [
883 |        "<Figure size 640x480 with 1 Axes>"
884 |       ]
885 |      },
886 |      "metadata": {},
887 |      "output_type": "display_data"
888 |     }
889 |    ],
890 |    "source": [
891 |     "sns.boxplot(x=\"sex\",y=\"age\",data=titanic)\n"
892 |    ]
893 |   },
894 |   {
895 |    "cell_type": "code",
896 |    "execution_count": null,
897 |    "id": "fa9504a6",
898 |    "metadata": {},
899 |    "outputs": [],
900 |    "source": [
901 |     "sns.boxplot(x=\"sex\",y=\"age\",data=titanic,hue=\"survived\")\n"
902 |    ]
903 |   }
904 |  ],
905 |  "metadata": {
906 |   "kernelspec": {
907 |    "display_name": "Python 3 (ipykernel)",
908 |    "language": "python",
909 |    "name": "python3"
910 |   },
911 |   "language_info": {
912 |    "codemirror_mode": {
913 |     "name": "ipython",
914 |     "version": 3
915 |    },
916 |    "file_extension": ".py",
917 |    "mimetype": "text/x-python",
918 |    "name": "python",
919 |    "nbconvert_exporter": "python",
920 |    "pygments_lexer": "ipython3",
921 |    "version": "3.9.13"
922 |   }
923 |  },
924 |  "nbformat": 4,
925 |  "nbformat_minor": 5
926 | }
927 | 


--------------------------------------------------------------------------------
/Assignment_6.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 2,
   6 |    "id": "c3f7d30d",
   7 |    "metadata": {
   8 |     "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
   9 |     "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
  10 |     "execution": {
  11 |      "iopub.execute_input": "2022-03-28T06:30:15.938539Z",
  12 |      "iopub.status.busy": "2022-03-28T06:30:15.936733Z",
  13 |      "iopub.status.idle": "2022-03-28T06:30:17.132091Z",
  14 |      "shell.execute_reply": "2022-03-28T06:30:17.132577Z",
  15 |      "shell.execute_reply.started": "2022-03-28T05:42:26.685951Z"
  16 |     },
  17 |     "papermill": {
  18 |      "duration": 1.224345,
  19 |      "end_time": "2022-03-28T06:30:17.132920",
  20 |      "exception": false,
  21 |      "start_time": "2022-03-28T06:30:15.908575",
  22 |      "status": "completed"
  23 |     },
  24 |     "tags": []
  25 |    },
  26 |    "outputs": [],
  27 |    "source": [
  28 |     "import numpy as np\n",
  29 |     "import pandas as pd\n",
  30 |     "from sklearn.model_selection import train_test_split\n",
  31 |     "from sklearn.naive_bayes import GaussianNB\n",
  32 |     "import matplotlib.pyplot as plt\n",
  33 |     "import seaborn as sns\n",
  34 |     "from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay,classification_report,accuracy_score, precision_score, recall_score, f1_score\n",
  35 |     "from sklearn.preprocessing import LabelEncoder"
  36 |    ]
  37 |   },
  38 |   {
  39 |    "cell_type": "markdown",
  40 |    "id": "17e8906c",
  41 |    "metadata": {
  42 |     "papermill": {
  43 |      "duration": 0.02312,
  44 |      "end_time": "2022-03-28T06:30:17.179907",
  45 |      "exception": false,
  46 |      "start_time": "2022-03-28T06:30:17.156787",
  47 |      "status": "completed"
  48 |     },
  49 |     "tags": []
  50 |    },
  51 |    "source": [
  52 |     "* Loading the dataset"
  53 |    ]
  54 |   },
  55 |   {
  56 |    "cell_type": "code",
  57 |    "execution_count": 5,
  58 |    "id": "176bdb3b",
  59 |    "metadata": {
  60 |     "execution": {
  61 |      "iopub.execute_input": "2022-03-28T06:30:17.230489Z",
  62 |      "iopub.status.busy": "2022-03-28T06:30:17.229515Z",
  63 |      "iopub.status.idle": "2022-03-28T06:30:17.260622Z",
  64 |      "shell.execute_reply": "2022-03-28T06:30:17.261171Z",
  65 |      "shell.execute_reply.started": "2022-03-28T05:42:28.173357Z"
  66 |     },
  67 |     "papermill": {
  68 |      "duration": 0.058053,
  69 |      "end_time": "2022-03-28T06:30:17.261336",
  70 |      "exception": false,
  71 |      "start_time": "2022-03-28T06:30:17.203283",
  72 |      "status": "completed"
  73 |     },
  74 |     "tags": []
  75 |    },
  76 |    "outputs": [
  77 |     {
  78 |      "data": {
  79 |       "text/html": [
  80 |        "<div>\n",
  81 |        "<style scoped>\n",
  82 |        "    .dataframe tbody tr th:only-of-type {\n",
  83 |        "        vertical-align: middle;\n",
  84 |        "    }\n",
  85 |        "\n",
  86 |        "    .dataframe tbody tr th {\n",
  87 |        "        vertical-align: top;\n",
  88 |        "    }\n",
  89 |        "\n",
  90 |        "    .dataframe thead th {\n",
  91 |        "        text-align: right;\n",
  92 |        "    }\n",
  93 |        "</style>\n",
  94 |        "<table border=\"1\" class=\"dataframe\">\n",
  95 |        "  <thead>\n",
  96 |        "    <tr style=\"text-align: right;\">\n",
  97 |        "      <th></th>\n",
  98 |        "      <th>Id</th>\n",
  99 |        "      <th>SepalLengthCm</th>\n",
 100 |        "      <th>SepalWidthCm</th>\n",
 101 |        "      <th>PetalLengthCm</th>\n",
 102 |        "      <th>PetalWidthCm</th>\n",
 103 |        "      <th>Species</th>\n",
 104 |        "    </tr>\n",
 105 |        "  </thead>\n",
 106 |        "  <tbody>\n",
 107 |        "    <tr>\n",
 108 |        "      <th>0</th>\n",
 109 |        "      <td>1</td>\n",
 110 |        "      <td>5.1</td>\n",
 111 |        "      <td>3.5</td>\n",
 112 |        "      <td>1.4</td>\n",
 113 |        "      <td>0.2</td>\n",
 114 |        "      <td>Iris-setosa</td>\n",
 115 |        "    </tr>\n",
 116 |        "    <tr>\n",
 117 |        "      <th>1</th>\n",
 118 |        "      <td>2</td>\n",
 119 |        "      <td>4.9</td>\n",
 120 |        "      <td>3.0</td>\n",
 121 |        "      <td>1.4</td>\n",
 122 |        "      <td>0.2</td>\n",
 123 |        "      <td>Iris-setosa</td>\n",
 124 |        "    </tr>\n",
 125 |        "    <tr>\n",
 126 |        "      <th>2</th>\n",
 127 |        "      <td>3</td>\n",
 128 |        "      <td>4.7</td>\n",
 129 |        "      <td>3.2</td>\n",
 130 |        "      <td>1.3</td>\n",
 131 |        "      <td>0.2</td>\n",
 132 |        "      <td>Iris-setosa</td>\n",
 133 |        "    </tr>\n",
 134 |        "    <tr>\n",
 135 |        "      <th>3</th>\n",
 136 |        "      <td>4</td>\n",
 137 |        "      <td>4.6</td>\n",
 138 |        "      <td>3.1</td>\n",
 139 |        "      <td>1.5</td>\n",
 140 |        "      <td>0.2</td>\n",
 141 |        "      <td>Iris-setosa</td>\n",
 142 |        "    </tr>\n",
 143 |        "    <tr>\n",
 144 |        "      <th>4</th>\n",
 145 |        "      <td>5</td>\n",
 146 |        "      <td>5.0</td>\n",
 147 |        "      <td>3.6</td>\n",
 148 |        "      <td>1.4</td>\n",
 149 |        "      <td>0.2</td>\n",
 150 |        "      <td>Iris-setosa</td>\n",
 151 |        "    </tr>\n",
 152 |        "  </tbody>\n",
 153 |        "</table>\n",
 154 |        "</div>"
 155 |       ],
 156 |       "text/plain": [
 157 |        "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
 158 |        "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
 159 |        "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
 160 |        "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
 161 |        "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
 162 |        "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
 163 |       ]
 164 |      },
 165 |      "execution_count": 5,
 166 |      "metadata": {},
 167 |      "output_type": "execute_result"
 168 |     }
 169 |    ],
 170 |    "source": [
 171 |     "data =  pd.read_csv(\"C:/Users/coeco/Downloads/Iris (1).csv\")\n",
 172 |     "data.head(5)"
 173 |    ]
 174 |   },
 175 |   {
 176 |    "cell_type": "markdown",
 177 |    "id": "ade4e19d",
 178 |    "metadata": {
 179 |     "papermill": {
 180 |      "duration": 0.023899,
 181 |      "end_time": "2022-03-28T06:30:17.309838",
 182 |      "exception": false,
 183 |      "start_time": "2022-03-28T06:30:17.285939",
 184 |      "status": "completed"
 185 |     },
 186 |     "tags": []
 187 |    },
 188 |    "source": [
 189 |     "* Checking Basic statistics of the dataset"
 190 |    ]
 191 |   },
 192 |   {
 193 |    "cell_type": "code",
 194 |    "execution_count": 6,
 195 |    "id": "890e89e9",
 196 |    "metadata": {
 197 |     "execution": {
 198 |      "iopub.execute_input": "2022-03-28T06:30:17.362102Z",
 199 |      "iopub.status.busy": "2022-03-28T06:30:17.361078Z",
 200 |      "iopub.status.idle": "2022-03-28T06:30:17.398105Z",
 201 |      "shell.execute_reply": "2022-03-28T06:30:17.398606Z",
 202 |      "shell.execute_reply.started": "2022-03-28T05:58:28.362031Z"
 203 |     },
 204 |     "papermill": {
 205 |      "duration": 0.064754,
 206 |      "end_time": "2022-03-28T06:30:17.398781",
 207 |      "exception": false,
 208 |      "start_time": "2022-03-28T06:30:17.334027",
 209 |      "status": "completed"
 210 |     },
 211 |     "tags": []
 212 |    },
 213 |    "outputs": [
 214 |     {
 215 |      "data": {
 216 |       "text/html": [
 217 |        "<div>\n",
 218 |        "<style scoped>\n",
 219 |        "    .dataframe tbody tr th:only-of-type {\n",
 220 |        "        vertical-align: middle;\n",
 221 |        "    }\n",
 222 |        "\n",
 223 |        "    .dataframe tbody tr th {\n",
 224 |        "        vertical-align: top;\n",
 225 |        "    }\n",
 226 |        "\n",
 227 |        "    .dataframe thead th {\n",
 228 |        "        text-align: right;\n",
 229 |        "    }\n",
 230 |        "</style>\n",
 231 |        "<table border=\"1\" class=\"dataframe\">\n",
 232 |        "  <thead>\n",
 233 |        "    <tr style=\"text-align: right;\">\n",
 234 |        "      <th></th>\n",
 235 |        "      <th>Id</th>\n",
 236 |        "      <th>SepalLengthCm</th>\n",
 237 |        "      <th>SepalWidthCm</th>\n",
 238 |        "      <th>PetalLengthCm</th>\n",
 239 |        "      <th>PetalWidthCm</th>\n",
 240 |        "      <th>Species</th>\n",
 241 |        "    </tr>\n",
 242 |        "  </thead>\n",
 243 |        "  <tbody>\n",
 244 |        "    <tr>\n",
 245 |        "      <th>count</th>\n",
 246 |        "      <td>150.000000</td>\n",
 247 |        "      <td>150.000000</td>\n",
 248 |        "      <td>150.000000</td>\n",
 249 |        "      <td>150.000000</td>\n",
 250 |        "      <td>150.000000</td>\n",
 251 |        "      <td>150</td>\n",
 252 |        "    </tr>\n",
 253 |        "    <tr>\n",
 254 |        "      <th>unique</th>\n",
 255 |        "      <td>NaN</td>\n",
 256 |        "      <td>NaN</td>\n",
 257 |        "      <td>NaN</td>\n",
 258 |        "      <td>NaN</td>\n",
 259 |        "      <td>NaN</td>\n",
 260 |        "      <td>3</td>\n",
 261 |        "    </tr>\n",
 262 |        "    <tr>\n",
 263 |        "      <th>top</th>\n",
 264 |        "      <td>NaN</td>\n",
 265 |        "      <td>NaN</td>\n",
 266 |        "      <td>NaN</td>\n",
 267 |        "      <td>NaN</td>\n",
 268 |        "      <td>NaN</td>\n",
 269 |        "      <td>Iris-setosa</td>\n",
 270 |        "    </tr>\n",
 271 |        "    <tr>\n",
 272 |        "      <th>freq</th>\n",
 273 |        "      <td>NaN</td>\n",
 274 |        "      <td>NaN</td>\n",
 275 |        "      <td>NaN</td>\n",
 276 |        "      <td>NaN</td>\n",
 277 |        "      <td>NaN</td>\n",
 278 |        "      <td>50</td>\n",
 279 |        "    </tr>\n",
 280 |        "    <tr>\n",
 281 |        "      <th>mean</th>\n",
 282 |        "      <td>75.500000</td>\n",
 283 |        "      <td>5.843333</td>\n",
 284 |        "      <td>3.054000</td>\n",
 285 |        "      <td>3.758667</td>\n",
 286 |        "      <td>1.198667</td>\n",
 287 |        "      <td>NaN</td>\n",
 288 |        "    </tr>\n",
 289 |        "    <tr>\n",
 290 |        "      <th>std</th>\n",
 291 |        "      <td>43.445368</td>\n",
 292 |        "      <td>0.828066</td>\n",
 293 |        "      <td>0.433594</td>\n",
 294 |        "      <td>1.764420</td>\n",
 295 |        "      <td>0.763161</td>\n",
 296 |        "      <td>NaN</td>\n",
 297 |        "    </tr>\n",
 298 |        "    <tr>\n",
 299 |        "      <th>min</th>\n",
 300 |        "      <td>1.000000</td>\n",
 301 |        "      <td>4.300000</td>\n",
 302 |        "      <td>2.000000</td>\n",
 303 |        "      <td>1.000000</td>\n",
 304 |        "      <td>0.100000</td>\n",
 305 |        "      <td>NaN</td>\n",
 306 |        "    </tr>\n",
 307 |        "    <tr>\n",
 308 |        "      <th>25%</th>\n",
 309 |        "      <td>38.250000</td>\n",
 310 |        "      <td>5.100000</td>\n",
 311 |        "      <td>2.800000</td>\n",
 312 |        "      <td>1.600000</td>\n",
 313 |        "      <td>0.300000</td>\n",
 314 |        "      <td>NaN</td>\n",
 315 |        "    </tr>\n",
 316 |        "    <tr>\n",
 317 |        "      <th>50%</th>\n",
 318 |        "      <td>75.500000</td>\n",
 319 |        "      <td>5.800000</td>\n",
 320 |        "      <td>3.000000</td>\n",
 321 |        "      <td>4.350000</td>\n",
 322 |        "      <td>1.300000</td>\n",
 323 |        "      <td>NaN</td>\n",
 324 |        "    </tr>\n",
 325 |        "    <tr>\n",
 326 |        "      <th>75%</th>\n",
 327 |        "      <td>112.750000</td>\n",
 328 |        "      <td>6.400000</td>\n",
 329 |        "      <td>3.300000</td>\n",
 330 |        "      <td>5.100000</td>\n",
 331 |        "      <td>1.800000</td>\n",
 332 |        "      <td>NaN</td>\n",
 333 |        "    </tr>\n",
 334 |        "    <tr>\n",
 335 |        "      <th>max</th>\n",
 336 |        "      <td>150.000000</td>\n",
 337 |        "      <td>7.900000</td>\n",
 338 |        "      <td>4.400000</td>\n",
 339 |        "      <td>6.900000</td>\n",
 340 |        "      <td>2.500000</td>\n",
 341 |        "      <td>NaN</td>\n",
 342 |        "    </tr>\n",
 343 |        "  </tbody>\n",
 344 |        "</table>\n",
 345 |        "</div>"
 346 |       ],
 347 |       "text/plain": [
 348 |        "                Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
 349 |        "count   150.000000     150.000000    150.000000     150.000000    150.000000   \n",
 350 |        "unique         NaN            NaN           NaN            NaN           NaN   \n",
 351 |        "top            NaN            NaN           NaN            NaN           NaN   \n",
 352 |        "freq           NaN            NaN           NaN            NaN           NaN   \n",
 353 |        "mean     75.500000       5.843333      3.054000       3.758667      1.198667   \n",
 354 |        "std      43.445368       0.828066      0.433594       1.764420      0.763161   \n",
 355 |        "min       1.000000       4.300000      2.000000       1.000000      0.100000   \n",
 356 |        "25%      38.250000       5.100000      2.800000       1.600000      0.300000   \n",
 357 |        "50%      75.500000       5.800000      3.000000       4.350000      1.300000   \n",
 358 |        "75%     112.750000       6.400000      3.300000       5.100000      1.800000   \n",
 359 |        "max     150.000000       7.900000      4.400000       6.900000      2.500000   \n",
 360 |        "\n",
 361 |        "            Species  \n",
 362 |        "count           150  \n",
 363 |        "unique            3  \n",
 364 |        "top     Iris-setosa  \n",
 365 |        "freq             50  \n",
 366 |        "mean            NaN  \n",
 367 |        "std             NaN  \n",
 368 |        "min             NaN  \n",
 369 |        "25%             NaN  \n",
 370 |        "50%             NaN  \n",
 371 |        "75%             NaN  \n",
 372 |        "max             NaN  "
 373 |       ]
 374 |      },
 375 |      "execution_count": 6,
 376 |      "metadata": {},
 377 |      "output_type": "execute_result"
 378 |     }
 379 |    ],
 380 |    "source": [
 381 |     "data.describe(include = 'all')"
 382 |    ]
 383 |   },
 384 |   {
 385 |    "cell_type": "code",
 386 |    "execution_count": 7,
 387 |    "id": "daaf340e",
 388 |    "metadata": {
 389 |     "execution": {
 390 |      "iopub.execute_input": "2022-03-28T06:30:17.452412Z",
 391 |      "iopub.status.busy": "2022-03-28T06:30:17.451459Z",
 392 |      "iopub.status.idle": "2022-03-28T06:30:17.466852Z",
 393 |      "shell.execute_reply": "2022-03-28T06:30:17.467362Z",
 394 |      "shell.execute_reply.started": "2022-03-28T05:42:28.261673Z"
 395 |     },
 396 |     "papermill": {
 397 |      "duration": 0.044028,
 398 |      "end_time": "2022-03-28T06:30:17.467532",
 399 |      "exception": false,
 400 |      "start_time": "2022-03-28T06:30:17.423504",
 401 |      "status": "completed"
 402 |     },
 403 |     "tags": []
 404 |    },
 405 |    "outputs": [
 406 |     {
 407 |      "name": "stdout",
 408 |      "output_type": "stream",
 409 |      "text": [
 410 |       "<class 'pandas.core.frame.DataFrame'>\n",
 411 |       "RangeIndex: 150 entries, 0 to 149\n",
 412 |       "Data columns (total 6 columns):\n",
 413 |       " #   Column         Non-Null Count  Dtype  \n",
 414 |       "---  ------         --------------  -----  \n",
 415 |       " 0   Id             150 non-null    int64  \n",
 416 |       " 1   SepalLengthCm  150 non-null    float64\n",
 417 |       " 2   SepalWidthCm   150 non-null    float64\n",
 418 |       " 3   PetalLengthCm  150 non-null    float64\n",
 419 |       " 4   PetalWidthCm   150 non-null    float64\n",
 420 |       " 5   Species        150 non-null    object \n",
 421 |       "dtypes: float64(4), int64(1), object(1)\n",
 422 |       "memory usage: 7.2+ KB\n"
 423 |      ]
 424 |     }
 425 |    ],
 426 |    "source": [
 427 |     "data.info()"
 428 |    ]
 429 |   },
 430 |   {
 431 |    "cell_type": "markdown",
 432 |    "id": "ab3fba23",
 433 |    "metadata": {
 434 |     "papermill": {
 435 |      "duration": 0.024936,
 436 |      "end_time": "2022-03-28T06:30:17.517609",
 437 |      "exception": false,
 438 |      "start_time": "2022-03-28T06:30:17.492673",
 439 |      "status": "completed"
 440 |     },
 441 |     "tags": []
 442 |    },
 443 |    "source": [
 444 |     "* Displaying Shape of the dataset and The Types of Species to Classify"
 445 |    ]
 446 |   },
 447 |   {
 448 |    "cell_type": "code",
 449 |    "execution_count": 8,
 450 |    "id": "540702cf",
 451 |    "metadata": {
 452 |     "execution": {
 453 |      "iopub.execute_input": "2022-03-28T06:30:17.572643Z",
 454 |      "iopub.status.busy": "2022-03-28T06:30:17.571647Z",
 455 |      "iopub.status.idle": "2022-03-28T06:30:17.580031Z",
 456 |      "shell.execute_reply": "2022-03-28T06:30:17.579499Z",
 457 |      "shell.execute_reply.started": "2022-03-28T05:42:28.28309Z"
 458 |     },
 459 |     "papermill": {
 460 |      "duration": 0.036677,
 461 |      "end_time": "2022-03-28T06:30:17.580186",
 462 |      "exception": false,
 463 |      "start_time": "2022-03-28T06:30:17.543509",
 464 |      "status": "completed"
 465 |     },
 466 |     "tags": []
 467 |    },
 468 |    "outputs": [
 469 |     {
 470 |      "name": "stdout",
 471 |      "output_type": "stream",
 472 |      "text": [
 473 |       "(150, 6)\n"
 474 |      ]
 475 |     },
 476 |     {
 477 |      "data": {
 478 |       "text/plain": [
 479 |        "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
 480 |       ]
 481 |      },
 482 |      "execution_count": 8,
 483 |      "metadata": {},
 484 |      "output_type": "execute_result"
 485 |     }
 486 |    ],
 487 |    "source": [
 488 |     "print(data.shape)\n",
 489 |     "data['Species'].unique()"
 490 |    ]
 491 |   },
 492 |   {
 493 |    "cell_type": "markdown",
 494 |    "id": "dbdb4dd4",
 495 |    "metadata": {
 496 |     "papermill": {
 497 |      "duration": 0.027855,
 498 |      "end_time": "2022-03-28T06:30:17.633877",
 499 |      "exception": false,
 500 |      "start_time": "2022-03-28T06:30:17.606022",
 501 |      "status": "completed"
 502 |     },
 503 |     "tags": []
 504 |    },
 505 |    "source": [
 506 |     "* Checking for Null values"
 507 |    ]
 508 |   },
 509 |   {
 510 |    "cell_type": "code",
 511 |    "execution_count": 9,
 512 |    "id": "f22a6c5b",
 513 |    "metadata": {
 514 |     "execution": {
 515 |      "iopub.execute_input": "2022-03-28T06:30:17.693626Z",
 516 |      "iopub.status.busy": "2022-03-28T06:30:17.692981Z",
 517 |      "iopub.status.idle": "2022-03-28T06:30:17.695531Z",
 518 |      "shell.execute_reply": "2022-03-28T06:30:17.696017Z",
 519 |      "shell.execute_reply.started": "2022-03-28T05:42:28.295215Z"
 520 |     },
 521 |     "papermill": {
 522 |      "duration": 0.036448,
 523 |      "end_time": "2022-03-28T06:30:17.696187",
 524 |      "exception": false,
 525 |      "start_time": "2022-03-28T06:30:17.659739",
 526 |      "status": "completed"
 527 |     },
 528 |     "tags": []
 529 |    },
 530 |    "outputs": [
 531 |     {
 532 |      "data": {
 533 |       "text/plain": [
 534 |        "Id               0\n",
 535 |        "SepalLengthCm    0\n",
 536 |        "SepalWidthCm     0\n",
 537 |        "PetalLengthCm    0\n",
 538 |        "PetalWidthCm     0\n",
 539 |        "Species          0\n",
 540 |        "dtype: int64"
 541 |       ]
 542 |      },
 543 |      "execution_count": 9,
 544 |      "metadata": {},
 545 |      "output_type": "execute_result"
 546 |     }
 547 |    ],
 548 |    "source": [
 549 |     "data.isnull().sum()"
 550 |    ]
 551 |   },
 552 |   {
 553 |    "cell_type": "markdown",
 554 |    "id": "70661c2b",
 555 |    "metadata": {
 556 |     "papermill": {
 557 |      "duration": 0.028302,
 558 |      "end_time": "2022-03-28T06:30:17.750717",
 559 |      "exception": false,
 560 |      "start_time": "2022-03-28T06:30:17.722415",
 561 |      "status": "completed"
 562 |     },
 563 |     "tags": []
 564 |    },
 565 |    "source": [
 566 |     "* As we see there are no missing values so lets split our dataset into training(x) and testing(y) "
 567 |    ]
 568 |   },
 569 |   {
 570 |    "cell_type": "code",
 571 |    "execution_count": 10,
 572 |    "id": "f4bbdd4a",
 573 |    "metadata": {
 574 |     "execution": {
 575 |      "iopub.execute_input": "2022-03-28T06:30:17.811527Z",
 576 |      "iopub.status.busy": "2022-03-28T06:30:17.810873Z",
 577 |      "iopub.status.idle": "2022-03-28T06:30:17.812420Z",
 578 |      "shell.execute_reply": "2022-03-28T06:30:17.812949Z",
 579 |      "shell.execute_reply.started": "2022-03-28T05:42:28.310548Z"
 580 |     },
 581 |     "papermill": {
 582 |      "duration": 0.035509,
 583 |      "end_time": "2022-03-28T06:30:17.813126",
 584 |      "exception": false,
 585 |      "start_time": "2022-03-28T06:30:17.777617",
 586 |      "status": "completed"
 587 |     },
 588 |     "tags": []
 589 |    },
 590 |    "outputs": [],
 591 |    "source": [
 592 |     "x = data.iloc[:,1:5]\n",
 593 |     "y = data.iloc[:,5:]"
 594 |    ]
 595 |   },
 596 |   {
 597 |    "cell_type": "markdown",
 598 |    "id": "59d4daa6",
 599 |    "metadata": {
 600 |     "papermill": {
 601 |      "duration": 0.026279,
 602 |      "end_time": "2022-03-28T06:30:17.866232",
 603 |      "exception": false,
 604 |      "start_time": "2022-03-28T06:30:17.839953",
 605 |      "status": "completed"
 606 |     },
 607 |     "tags": []
 608 |    },
 609 |    "source": [
 610 |     "* Encoding the Species column"
 611 |    ]
 612 |   },
 613 |   {
 614 |    "cell_type": "code",
 615 |    "execution_count": 11,
 616 |    "id": "7e3a839d",
 617 |    "metadata": {
 618 |     "execution": {
 619 |      "iopub.execute_input": "2022-03-28T06:30:17.923691Z",
 620 |      "iopub.status.busy": "2022-03-28T06:30:17.923053Z",
 621 |      "iopub.status.idle": "2022-03-28T06:30:17.929861Z",
 622 |      "shell.execute_reply": "2022-03-28T06:30:17.930610Z",
 623 |      "shell.execute_reply.started": "2022-03-28T05:42:28.322242Z"
 624 |     },
 625 |     "papermill": {
 626 |      "duration": 0.037381,
 627 |      "end_time": "2022-03-28T06:30:17.930843",
 628 |      "exception": false,
 629 |      "start_time": "2022-03-28T06:30:17.893462",
 630 |      "status": "completed"
 631 |     },
 632 |     "tags": []
 633 |    },
 634 |    "outputs": [
 635 |     {
 636 |      "name": "stderr",
 637 |      "output_type": "stream",
 638 |      "text": [
 639 |       "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:115: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
 640 |       "  y = column_or_1d(y, warn=True)\n"
 641 |      ]
 642 |     }
 643 |    ],
 644 |    "source": [
 645 |     "encode = LabelEncoder()\n",
 646 |     "y = encode.fit_transform(y)"
 647 |    ]
 648 |   },
 649 |   {
 650 |    "cell_type": "markdown",
 651 |    "id": "d661ff20",
 652 |    "metadata": {
 653 |     "papermill": {
 654 |      "duration": 0.026649,
 655 |      "end_time": "2022-03-28T06:30:17.986559",
 656 |      "exception": false,
 657 |      "start_time": "2022-03-28T06:30:17.959910",
 658 |      "status": "completed"
 659 |     },
 660 |     "tags": []
 661 |    },
 662 |    "source": [
 663 |     "* Spliting training and testing dataset by 70-30 "
 664 |    ]
 665 |   },
 666 |   {
 667 |    "cell_type": "code",
 668 |    "execution_count": 12,
 669 |    "id": "9892536e",
 670 |    "metadata": {
 671 |     "execution": {
 672 |      "iopub.execute_input": "2022-03-28T06:30:18.043949Z",
 673 |      "iopub.status.busy": "2022-03-28T06:30:18.043266Z",
 674 |      "iopub.status.idle": "2022-03-28T06:30:18.049681Z",
 675 |      "shell.execute_reply": "2022-03-28T06:30:18.049123Z",
 676 |      "shell.execute_reply.started": "2022-03-28T05:42:28.337921Z"
 677 |     },
 678 |     "papermill": {
 679 |      "duration": 0.036124,
 680 |      "end_time": "2022-03-28T06:30:18.049837",
 681 |      "exception": false,
 682 |      "start_time": "2022-03-28T06:30:18.013713",
 683 |      "status": "completed"
 684 |     },
 685 |     "tags": []
 686 |    },
 687 |    "outputs": [],
 688 |    "source": [
 689 |     "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.3,random_state = 0)"
 690 |    ]
 691 |   },
 692 |   {
 693 |    "cell_type": "markdown",
 694 |    "id": "8a21ba02",
 695 |    "metadata": {
 696 |     "papermill": {
 697 |      "duration": 0.026949,
 698 |      "end_time": "2022-03-28T06:30:18.104532",
 699 |      "exception": false,
 700 |      "start_time": "2022-03-28T06:30:18.077583",
 701 |      "status": "completed"
 702 |     },
 703 |     "tags": []
 704 |    },
 705 |    "source": [
 706 |     "### Preparing Naive Bayes Model"
 707 |    ]
 708 |   },
 709 |   {
 710 |    "cell_type": "code",
 711 |    "execution_count": 13,
 712 |    "id": "6d7b7603",
 713 |    "metadata": {
 714 |     "execution": {
 715 |      "iopub.execute_input": "2022-03-28T06:30:18.169126Z",
 716 |      "iopub.status.busy": "2022-03-28T06:30:18.168415Z",
 717 |      "iopub.status.idle": "2022-03-28T06:30:18.171271Z",
 718 |      "shell.execute_reply": "2022-03-28T06:30:18.171740Z",
 719 |      "shell.execute_reply.started": "2022-03-28T05:42:28.351393Z"
 720 |     },
 721 |     "papermill": {
 722 |      "duration": 0.040105,
 723 |      "end_time": "2022-03-28T06:30:18.171932",
 724 |      "exception": false,
 725 |      "start_time": "2022-03-28T06:30:18.131827",
 726 |      "status": "completed"
 727 |     },
 728 |     "tags": []
 729 |    },
 730 |    "outputs": [],
 731 |    "source": [
 732 |     "naive_bayes = GaussianNB()\n",
 733 |     "naive_bayes.fit(x_train,y_train)\n",
 734 |     "pred = naive_bayes.predict(x_test)"
 735 |    ]
 736 |   },
 737 |   {
 738 |    "cell_type": "code",
 739 |    "execution_count": 14,
 740 |    "id": "391c07f1",
 741 |    "metadata": {
 742 |     "execution": {
 743 |      "iopub.execute_input": "2022-03-28T06:30:18.230215Z",
 744 |      "iopub.status.busy": "2022-03-28T06:30:18.229367Z",
 745 |      "iopub.status.idle": "2022-03-28T06:30:18.235141Z",
 746 |      "shell.execute_reply": "2022-03-28T06:30:18.234518Z",
 747 |      "shell.execute_reply.started": "2022-03-28T05:42:28.364784Z"
 748 |     },
 749 |     "papermill": {
 750 |      "duration": 0.036196,
 751 |      "end_time": "2022-03-28T06:30:18.235288",
 752 |      "exception": false,
 753 |      "start_time": "2022-03-28T06:30:18.199092",
 754 |      "status": "completed"
 755 |     },
 756 |     "tags": []
 757 |    },
 758 |    "outputs": [
 759 |     {
 760 |      "data": {
 761 |       "text/plain": [
 762 |        "array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1, 0, 0, 2, 1,\n",
 763 |        "       0, 0, 2, 0, 0, 1, 1, 0, 2, 1, 0, 2, 2, 1, 0, 1, 1, 1, 2, 0, 2, 0,\n",
 764 |        "       0])"
 765 |       ]
 766 |      },
 767 |      "execution_count": 14,
 768 |      "metadata": {},
 769 |      "output_type": "execute_result"
 770 |     }
 771 |    ],
 772 |    "source": [
 773 |     "pred"
 774 |    ]
 775 |   },
 776 |   {
 777 |    "cell_type": "code",
 778 |    "execution_count": 15,
 779 |    "id": "00aff646",
 780 |    "metadata": {
 781 |     "execution": {
 782 |      "iopub.execute_input": "2022-03-28T06:30:18.296215Z",
 783 |      "iopub.status.busy": "2022-03-28T06:30:18.295521Z",
 784 |      "iopub.status.idle": "2022-03-28T06:30:18.298105Z",
 785 |      "shell.execute_reply": "2022-03-28T06:30:18.298651Z",
 786 |      "shell.execute_reply.started": "2022-03-28T06:07:17.175863Z"
 787 |     },
 788 |     "papermill": {
 789 |      "duration": 0.035708,
 790 |      "end_time": "2022-03-28T06:30:18.298839",
 791 |      "exception": false,
 792 |      "start_time": "2022-03-28T06:30:18.263131",
 793 |      "status": "completed"
 794 |     },
 795 |     "tags": []
 796 |    },
 797 |    "outputs": [
 798 |     {
 799 |      "data": {
 800 |       "text/plain": [
 801 |        "array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1, 0, 0, 2, 1,\n",
 802 |        "       0, 0, 2, 0, 0, 1, 1, 0, 2, 1, 0, 2, 2, 1, 0, 1, 1, 1, 2, 0, 2, 0,\n",
 803 |        "       0])"
 804 |       ]
 805 |      },
 806 |      "execution_count": 15,
 807 |      "metadata": {},
 808 |      "output_type": "execute_result"
 809 |     }
 810 |    ],
 811 |    "source": [
 812 |     "y_test"
 813 |    ]
 814 |   },
 815 |   {
 816 |    "cell_type": "markdown",
 817 |    "id": "f8c4c256",
 818 |    "metadata": {
 819 |     "papermill": {
 820 |      "duration": 0.027345,
 821 |      "end_time": "2022-03-28T06:30:18.354044",
 822 |      "exception": false,
 823 |      "start_time": "2022-03-28T06:30:18.326699",
 824 |      "status": "completed"
 825 |     },
 826 |     "tags": []
 827 |    },
 828 |    "source": [
 829 |     "* Plotting Confusion Matrix "
 830 |    ]
 831 |   },
 832 |   {
 833 |    "cell_type": "code",
 834 |    "execution_count": 16,
 835 |    "id": "487a2eca",
 836 |    "metadata": {
 837 |     "execution": {
 838 |      "iopub.execute_input": "2022-03-28T06:30:18.418013Z",
 839 |      "iopub.status.busy": "2022-03-28T06:30:18.417236Z",
 840 |      "iopub.status.idle": "2022-03-28T06:30:18.420675Z",
 841 |      "shell.execute_reply": "2022-03-28T06:30:18.421213Z",
 842 |      "shell.execute_reply.started": "2022-03-28T05:42:28.393178Z"
 843 |     },
 844 |     "papermill": {
 845 |      "duration": 0.039345,
 846 |      "end_time": "2022-03-28T06:30:18.421396",
 847 |      "exception": false,
 848 |      "start_time": "2022-03-28T06:30:18.382051",
 849 |      "status": "completed"
 850 |     },
 851 |     "tags": []
 852 |    },
 853 |    "outputs": [
 854 |     {
 855 |      "name": "stdout",
 856 |      "output_type": "stream",
 857 |      "text": [
 858 |       "[[16  0  0]\n",
 859 |       " [ 0 18  0]\n",
 860 |       " [ 0  0 11]]\n"
 861 |      ]
 862 |     }
 863 |    ],
 864 |    "source": [
 865 |     "matrix =  confusion_matrix(y_test,pred,labels = naive_bayes.classes_)\n",
 866 |     "print(matrix)\n",
 867 |     "\n",
 868 |     "tp, fn, fp, tn = confusion_matrix(y_test,pred,labels=[1,0]).reshape(-1)"
 869 |    ]
 870 |   },
 871 |   {
 872 |    "cell_type": "code",
 873 |    "execution_count": 17,
 874 |    "id": "b4ffbe26",
 875 |    "metadata": {
 876 |     "execution": {
 877 |      "iopub.execute_input": "2022-03-28T06:30:18.484841Z",
 878 |      "iopub.status.busy": "2022-03-28T06:30:18.483746Z",
 879 |      "iopub.status.idle": "2022-03-28T06:30:18.751506Z",
 880 |      "shell.execute_reply": "2022-03-28T06:30:18.750809Z",
 881 |      "shell.execute_reply.started": "2022-03-28T05:52:25.81269Z"
 882 |     },
 883 |     "papermill": {
 884 |      "duration": 0.302074,
 885 |      "end_time": "2022-03-28T06:30:18.751646",
 886 |      "exception": false,
 887 |      "start_time": "2022-03-28T06:30:18.449572",
 888 |      "status": "completed"
 889 |     },
 890 |     "tags": []
 891 |    },
 892 |    "outputs": [
 893 |     {
 894 |      "data": {
 895 |       "image/png": 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\n",
 896 |       "text/plain": [
 897 |        "<Figure size 640x480 with 2 Axes>"
 898 |       ]
 899 |      },
 900 |      "metadata": {},
 901 |      "output_type": "display_data"
 902 |     }
 903 |    ],
 904 |    "source": [
 905 |     "conf_matrix = ConfusionMatrixDisplay(confusion_matrix=matrix,display_labels=naive_bayes.classes_)\n",
 906 |     "conf_matrix.plot(cmap=plt.cm.YlGn)\n",
 907 |     "plt.show()"
 908 |    ]
 909 |   },
 910 |   {
 911 |    "cell_type": "markdown",
 912 |    "id": "e67b5772",
 913 |    "metadata": {
 914 |     "papermill": {
 915 |      "duration": 0.029507,
 916 |      "end_time": "2022-03-28T06:30:18.810296",
 917 |      "exception": false,
 918 |      "start_time": "2022-03-28T06:30:18.780789",
 919 |      "status": "completed"
 920 |     },
 921 |     "tags": []
 922 |    },
 923 |    "source": [
 924 |     "* Evaluating our model and calculating TN,FN,TP,FP Accuracy,Recall,Precision,ErrorRate,"
 925 |    ]
 926 |   },
 927 |   {
 928 |    "cell_type": "code",
 929 |    "execution_count": 18,
 930 |    "id": "4e802416",
 931 |    "metadata": {
 932 |     "execution": {
 933 |      "iopub.execute_input": "2022-03-28T06:30:18.874162Z",
 934 |      "iopub.status.busy": "2022-03-28T06:30:18.873093Z",
 935 |      "iopub.status.idle": "2022-03-28T06:30:18.881353Z",
 936 |      "shell.execute_reply": "2022-03-28T06:30:18.881875Z",
 937 |      "shell.execute_reply.started": "2022-03-28T05:49:18.507494Z"
 938 |     },
 939 |     "papermill": {
 940 |      "duration": 0.042481,
 941 |      "end_time": "2022-03-28T06:30:18.882045",
 942 |      "exception": false,
 943 |      "start_time": "2022-03-28T06:30:18.839564",
 944 |      "status": "completed"
 945 |     },
 946 |     "tags": []
 947 |    },
 948 |    "outputs": [
 949 |     {
 950 |      "name": "stdout",
 951 |      "output_type": "stream",
 952 |      "text": [
 953 |       "              precision    recall  f1-score   support\n",
 954 |       "\n",
 955 |       "           0       1.00      1.00      1.00        16\n",
 956 |       "           1       1.00      1.00      1.00        18\n",
 957 |       "           2       1.00      1.00      1.00        11\n",
 958 |       "\n",
 959 |       "    accuracy                           1.00        45\n",
 960 |       "   macro avg       1.00      1.00      1.00        45\n",
 961 |       "weighted avg       1.00      1.00      1.00        45\n",
 962 |       "\n"
 963 |      ]
 964 |     }
 965 |    ],
 966 |    "source": [
 967 |     "print(classification_report(y_test,pred))"
 968 |    ]
 969 |   },
 970 |   {
 971 |    "cell_type": "code",
 972 |    "execution_count": 19,
 973 |    "id": "5c090c10",
 974 |    "metadata": {
 975 |     "execution": {
 976 |      "iopub.execute_input": "2022-03-28T06:30:18.946931Z",
 977 |      "iopub.status.busy": "2022-03-28T06:30:18.945950Z",
 978 |      "iopub.status.idle": "2022-03-28T06:30:18.955196Z",
 979 |      "shell.execute_reply": "2022-03-28T06:30:18.954520Z",
 980 |      "shell.execute_reply.started": "2022-03-28T05:49:06.971284Z"
 981 |     },
 982 |     "papermill": {
 983 |      "duration": 0.042721,
 984 |      "end_time": "2022-03-28T06:30:18.955341",
 985 |      "exception": false,
 986 |      "start_time": "2022-03-28T06:30:18.912620",
 987 |      "status": "completed"
 988 |     },
 989 |     "tags": []
 990 |    },
 991 |    "outputs": [
 992 |     {
 993 |      "name": "stdout",
 994 |      "output_type": "stream",
 995 |      "text": [
 996 |       "\n",
 997 |       "Accuracy: 1.00\n",
 998 |       "Error Rate:  0.0\n",
 999 |       "Sensitivity (Recall or True positive rate) : 1.0\n",
1000 |       "Specificity (True negative rate) : 1.0\n",
1001 |       "Precision (Positive predictive value) : 1.0\n",
1002 |       "False Positive Rate : 0.0\n"
1003 |      ]
1004 |     }
1005 |    ],
1006 |    "source": [
1007 |     "print('\\nAccuracy: {:.2f}'.format(accuracy_score(y_test,pred)))\n",
1008 |     "print('Error Rate: ',(fp+fn)/(tp+tn+fn+fp))\n",
1009 |     "print('Sensitivity (Recall or True positive rate) :',tp/(tp+fn))\n",
1010 |     "print('Specificity (True negative rate) :',tn/(fp+tn))\n",
1011 |     "print('Precision (Positive predictive value) :',tp/(tp+fp))\n",
1012 |     "print('False Positive Rate :',fp/(tn+fp))"
1013 |    ]
1014 |   },
1015 |   {
1016 |    "cell_type": "code",
1017 |    "execution_count": null,
1018 |    "id": "7701a5bb",
1019 |    "metadata": {},
1020 |    "outputs": [],
1021 |    "source": []
1022 |   }
1023 |  ],
1024 |  "metadata": {
1025 |   "kernelspec": {
1026 |    "display_name": "Python 3 (ipykernel)",
1027 |    "language": "python",
1028 |    "name": "python3"
1029 |   },
1030 |   "language_info": {
1031 |    "codemirror_mode": {
1032 |     "name": "ipython",
1033 |     "version": 3
1034 |    },
1035 |    "file_extension": ".py",
1036 |    "mimetype": "text/x-python",
1037 |    "name": "python",
1038 |    "nbconvert_exporter": "python",
1039 |    "pygments_lexer": "ipython3",
1040 |    "version": "3.9.13"
1041 |   },
1042 |   "papermill": {
1043 |    "default_parameters": {},
1044 |    "duration": 13.707806,
1045 |    "end_time": "2022-03-28T06:30:19.758101",
1046 |    "environment_variables": {},
1047 |    "exception": null,
1048 |    "input_path": "__notebook__.ipynb",
1049 |    "output_path": "__notebook__.ipynb",
1050 |    "parameters": {},
1051 |    "start_time": "2022-03-28T06:30:06.050295",
1052 |    "version": "2.3.3"
1053 |   }
1054 |  },
1055 |  "nbformat": 4,
1056 |  "nbformat_minor": 5
1057 | }
1058 | 


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