Has to be done in Jupyter Notebook. This is a project using Python programming language to...

Question

Has to be done in Jupyter Notebook.

This is a project using Python programming language to analyse a specific problem in the following areas, such as Pharmacy, Library, Holiday Booking System, Medical Practice, Concert hall, Motor mechanic, Sales, Customers behaviour, Primary School, Role-playing game and manufacturing etc. Your group may choose data from any other category based on your interest from Kaggle (https://www.kaggle.com/) or UCI (https://archive.ics.uci.edu/ml/index.php) or any other repository. The dataset should have

at least 8000 rows and 10 columns (for example, type of variables may be categorical, continuous and discrete) after cleaning and there is not any maximum limit for data records. Your group may use any data set and complete the following tasks as mentioned below

6) Develop Data Exploratory analysis (EDA) report which shows that the numeric variables in the data set and exhibit no obvious association.

7) Based on your EDA, identify interesting sub-groups of records within the data set that would be worth further investigation.

8) Apply one-hot encoding to categorical variables (at least one variable use from the data set) and discuss the benefits of one-hot encoding to understand the categorical data.

9) Apply PCA on your data set by considering any number of components (at least 2 components must be chosen). Write a short profile of the first few components

extracted based on your understanding.

10) What is the objective of dimensionality reduction? Suppose that you perform the PCA using three components. Considering the following information, which variable or variables might be well advised to omit from the PCA, and why?

The Data and visualizations also need to answer the following questions as I have to do a report about it after.

What countries have the best ranked wines on average based on the point ranking system? (Top 5 countries)

What countries offer a better price/quality ratio based on price and the points system?

Is there a correlation between price and the quality of the wines?

What variety of wine has the best ranking on average?

What region offers the best ranked wines on average and which one offers the worse ranked wines on average?

exploration-ca-latest-version-crnlp4xl.ipynb winemag-datafirst150k-dgo0papg.csv

Neha · Accepted Answer

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra
",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
",
    "import matplotlib.pyplot as plt
",
    "from sklearn.metrics import confusion_matrix
",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv("winemag-datafirst150k.csv")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
",
       "
",
       "    .dataframe tbody tr th:only-of-type {
",
       "        vertical-align: middle;
",
       "    }
",
       "
",
       "    .dataframe tbody tr th {
",
       "        vertical-align: top;
",
       "    }
",
       "
",
       "    .dataframe thead th {
",
       "        text-align: right;
",
       "    }
",
       "
",
       "
",
       "  
",
       "    
",
       "      
",
       "      Unnamed: 0
",
       "      country
",
       "      description
",
       "      designation
",
       "      points
",
       "      price
",
       "      province
",
       "      region_1
",
       "      region_2
",
       "      variety
",
       "      winery
",
       "    
",
       "  
",
       "  
",
       "    
",
       "      0
",
       "      0
",
       "      US
",
       "      This tremendous 100% varietal wine hails from ...
",
       "      Martha's Vineyard
",
       "      96
",
       "      235.0
",
       "      California
",
       "      Napa Valley
",
       "      Napa
",
       "      Cabernet Sauvignon
",
       "      Heitz
",
       "    
",
       "    
",
       "      1
",
       "      1
",
       "      Spain
",
       "      Ripe aromas of fig, blackberry and cassis are ...
",
       "      Carodorum Selección Especial Reserva
",
       "      96
",
       "      110.0
",
       "      Northern Spain
",
       "      Toro
",
       "      NaN
",
       "      Tinta de Toro
",
       "      Bodega Carmen Rodríguez
",
       "    
",
       "    
",
       "      2
",
       "      2
",
       "      US
",
       "      Mac Watson honors the memory of a wine once ma...
",
       "      Special Selected Late Harvest
",
       "      96
",
       "      90.0
",
       "      California
",
       "      Knights Valley
",
       "      Sonoma
",
       "      Sauvignon Blanc
",
       "      Macauley
",
       "    
",
       "    
",
       "      3
",
       "      3
",
       "      US
",
       "      This spent 20 months in 30% new French oak, an...
",
       "      Reserve
",
       "      96
",
       "      65.0
",
       "      Oregon
",
       "      Willamette Valley
",
       "      Willamette Valley
",
       "      Pinot Noir
",
       "      Ponzi
",
       "    
",
       "    
",
       "      4
",
       "      4
",
       "      France
",
       "      This is the top wine from La Bégude, named aft...
",
       "      La Brûlade
",
       "      95
",
       "      66.0
",
       "      Provence
",
       "      Bandol
",
       "      NaN
",
       "      Provence red blend
",
       "      Domaine de la Bégude
",
       "    
",
       "  
",
       "
",
       ""
      ],
      "text/plain": [
       "   Unnamed: 0 country                                        description  \
",
       "0           0      US  This tremendous 100% varietal wine hails from ...   
",
       "1           1   Spain  Ripe aromas of fig, blackberry and cassis are ...   
",
       "2           2      US  Mac Watson honors the memory of a wine once ma...   
",
       "3           3      US  This spent 20 months in 30% new French oak, an...   
",
       "4           4  France  This is the top wine from La Bégude, named aft...   
",
       "
",
       "                            designation  points  price        province  \
",
       "0                     Martha's Vineyard      96  235.0      California   
",
       "1  Carodorum Selección Especial Reserva      96  110.0  Northern Spain   
",
       "2         Special Selected Late Harvest      96   90.0      California   
",
       "3                               Reserve      96   65.0          Oregon   
",
       "4                            La Brûlade      95   66.0        Provence   
",
       "
",
       "            region_1           region_2             variety  \
",
       "0        Napa Valley               Napa  Cabernet Sauvignon   
",
       "1               Toro                NaN       Tinta de Toro   
",
       "2     Knights Valley             Sonoma     Sauvignon Blanc   
",
       "3  Willamette Valley  Willamette Valley          Pinot Noir   
",
       "4             Bandol                NaN  Provence red blend   
",
       "
",
       "                    winery  
",
       "0                    Heitz  
",
       "1  Bodega Carmen Rodríguez  
",
       "2                 Macauley  
",
       "3                    Ponzi  
",
       "4     Domaine de la Bégude  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150930, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0         0
",
       "country            5
",
       "description        0
",
       "designation    45735
",
       "points             0
",
       "price          13695
",
       "province           5
",
       "region_1       25060
",
       "region_2       89977
",
       "variety            0
",
       "winery             0
",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'description', 'designation', 'province', 'region_1',
",
       "       'region_2', 'variety', 'winery'],
",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features = df.select_dtypes(include=[np.object])
",
    "categorical_features.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'points', 'price'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_features = df.select_dtypes(include=[np.number])
",
    "numeric_features.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'country', 'description', 'designation', 'points',
",
       "       'price', 'province', 'region_1', 'region_2', 'variety', 'winery'],
",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    137235.000000
",
       "mean         33.131482
",
       "std          36.322536
",
       "min           4.000000
",
       "25%          16.000000
",
       "50%          24.000000
",
       "75%          40.000000
",
       "max        2300.000000
",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 11.820100
",
      "Kurtosis: 372.824222
"
     ]
    }
   ],
   "source": [
    "print("Skewness: %f" % df['price'].skew())
",
    "print("Kurtosis: %f" % df['price'].kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['country'].value_counts().head(10).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df['country'].value_counts().head(10)/len(df)).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df['province'].value_counts().head(10) / len(df)).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='points', y='price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hexbin(x='price', y='points')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['price'] "
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['points'].value_counts().sort_index().plot.bar(
",
    "    figsize=(12, 6),
",
    "    color='mediumvioletred',
",
    "    fontsize=16
",
    ")
",
    "ax.set_title("Rankings Given by Wine Magazine", fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png":

Has to be done in Jupyter Notebook. This is a project using Python programming language to analyse a specific problem in the following areas, such as Pharmacy, Library, Holiday Booking System, Medical...

Answer To: Has to be done in Jupyter Notebook. This is a project using Python programming language to analyse a...

Answer To This Question Is Available To Download

Related Questions & Answers

Submit New Assignment

	Unnamed: 0	country	description	designation	points	price	province	region_1	region_2	variety	winery
0	0	US	This tremendous 100% varietal wine hails from ...	Martha's Vineyard	96	235.0	California	Napa Valley	Napa	Cabernet Sauvignon	Heitz
1	1	Spain	Ripe aromas of fig, blackberry and cassis are ...	Carodorum Selección Especial Reserva	96	110.0	Northern Spain	Toro	NaN	Tinta de Toro	Bodega Carmen Rodríguez
2	2	US	Mac Watson honors the memory of a wine once ma...	Special Selected Late Harvest	96	90.0	California	Knights Valley	Sonoma	Sauvignon Blanc	Macauley
3	3	US	This spent 20 months in 30% new French oak, an...	Reserve	96	65.0	Oregon	Willamette Valley	Willamette Valley	Pinot Noir	Ponzi
4	4	France	This is the top wine from La Bégude, named aft...	La Brûlade	95	66.0	Provence	Bandol	NaN	Provence red blend	Domaine de la Bégude