The document discusses a dataset from the UCI Machine Learning Repository that contains automobile data. The dataset includes 26 attributes describing the characteristics of different automobile models, their specifications, insurance information, and normalized losses compared to other models. The objective is to perform exploratory data analysis on the dataset to understand relationships between features, and to predict car prices using regression analysis.
This technical catalog provides specifications for Poclain Hydraulics modular hydraulic motors models MS18 and MSE18. It includes information on motor characteristics, dimensions, support types, load curves, valving systems, options and the model code structure. The document is intended to help machine manufacturers incorporate these products and ensure optimal operation in a safe manner. Metric units are used and safety comments are indicated.
This document compares the cost estimates of a railway doubling project from the initial Detailed Project Report (DPR) to two subsequent revised estimates. The total estimated cost increased from ₹750.56 billion in the DPR to ₹1293.72 billion in the first revised estimate, an increase of 72.37%. It further increased to ₹2018.23 billion in the second revised estimate, up another 15.12% from the first. Most of the increase was due to price escalation, though some was also attributed to changes in project scope.
This document provides information on oil product prices in Korea, including:
1. An overview of oil price systems and liberalization trends in Korea.
2. Average annual prices of major oil products from 1964-1996. Prices of gasoline, kerosene, and diesel increased sharply in the 1970s due to oil crises and price fluctuations have continued since then.
3. Monthly average prices of major oil products from 2008-2009, which decreased in early 2008 before rising again.
The document contains detailed tables and historical data on oil product prices in Korea over several decades.
Caterpillar cat dp45 k forklift lift trucks service repair manual snet19c 800...fjjsekkmdmes
This document provides a service manual for truck models GP40K, GP40KL, GP45K, GP50K, DP40K, DP40KL, DP45K and DP50K. It includes 3 sections - general information, specifications and technical data for major components like the engine, transmission, brakes and other systems. Dimensions, images and exploded diagrams are also provided to support servicing and maintenance of the trucks.
This document provides a product support program for repairing cracked reach booms on certain 330C and 330D excavators. It outlines the affected product models and identification numbers, describes the problem of cracks developing in the reach boom, and provides notes on claiming the repair under the program using specific part numbers, group numbers, warranty codes, and termination dates. The program is available for administering repairs after a failure occurs at the dealer's discretion.
The document describes Harmonic Drive's new CSG-LW gear unit, which is 30% lighter than the standard CSG-2UH unit while maintaining 30% more torque. This makes it well-suited for applications requiring high torque density like robotics. The document provides specifications, dimensions, torque and efficiency data for the CSG-LW series.
Harmonic Drive has introduced a new lightweight version of its SHG and SHF hollow shaft gear units that is 20% lighter than previous models. This was achieved through new lightweight materials and optimized design without reducing torque ratings or changing interface dimensions. The lighter weight improves torque density and makes the gear units well-suited for applications like industrial and mobile robots by allowing for higher payloads, acceleration, and longer battery life.
This document provides specifications for the 2002 Impreza, including dimensions, engine details, electrical components, transmission information, steering, suspension, brakes, tires, and fluid capacities. It covers the 1.6L, 2.0L non-turbo, 2.0L turbo, 2.5L, and STi models. The specifications are grouped into sections and include metrics such as bore/stroke, displacement, output, battery type and more.
This technical catalog provides specifications for Poclain Hydraulics modular hydraulic motors models MS18 and MSE18. It includes information on motor characteristics, dimensions, support types, load curves, valving systems, options and the model code structure. The document is intended to help machine manufacturers incorporate these products and ensure optimal operation in a safe manner. Metric units are used and safety comments are indicated.
This document compares the cost estimates of a railway doubling project from the initial Detailed Project Report (DPR) to two subsequent revised estimates. The total estimated cost increased from ₹750.56 billion in the DPR to ₹1293.72 billion in the first revised estimate, an increase of 72.37%. It further increased to ₹2018.23 billion in the second revised estimate, up another 15.12% from the first. Most of the increase was due to price escalation, though some was also attributed to changes in project scope.
This document provides information on oil product prices in Korea, including:
1. An overview of oil price systems and liberalization trends in Korea.
2. Average annual prices of major oil products from 1964-1996. Prices of gasoline, kerosene, and diesel increased sharply in the 1970s due to oil crises and price fluctuations have continued since then.
3. Monthly average prices of major oil products from 2008-2009, which decreased in early 2008 before rising again.
The document contains detailed tables and historical data on oil product prices in Korea over several decades.
Caterpillar cat dp45 k forklift lift trucks service repair manual snet19c 800...fjjsekkmdmes
This document provides a service manual for truck models GP40K, GP40KL, GP45K, GP50K, DP40K, DP40KL, DP45K and DP50K. It includes 3 sections - general information, specifications and technical data for major components like the engine, transmission, brakes and other systems. Dimensions, images and exploded diagrams are also provided to support servicing and maintenance of the trucks.
This document provides a product support program for repairing cracked reach booms on certain 330C and 330D excavators. It outlines the affected product models and identification numbers, describes the problem of cracks developing in the reach boom, and provides notes on claiming the repair under the program using specific part numbers, group numbers, warranty codes, and termination dates. The program is available for administering repairs after a failure occurs at the dealer's discretion.
The document describes Harmonic Drive's new CSG-LW gear unit, which is 30% lighter than the standard CSG-2UH unit while maintaining 30% more torque. This makes it well-suited for applications requiring high torque density like robotics. The document provides specifications, dimensions, torque and efficiency data for the CSG-LW series.
Harmonic Drive has introduced a new lightweight version of its SHG and SHF hollow shaft gear units that is 20% lighter than previous models. This was achieved through new lightweight materials and optimized design without reducing torque ratings or changing interface dimensions. The lighter weight improves torque density and makes the gear units well-suited for applications like industrial and mobile robots by allowing for higher payloads, acceleration, and longer battery life.
This document provides specifications for the 2002 Impreza, including dimensions, engine details, electrical components, transmission information, steering, suspension, brakes, tires, and fluid capacities. It covers the 1.6L, 2.0L non-turbo, 2.0L turbo, 2.5L, and STi models. The specifications are grouped into sections and include metrics such as bore/stroke, displacement, output, battery type and more.
This document contains information about Heubach International, an ISO 9001:2008 certified company that exports, stocks, and supplies various ferrous and non-ferrous raw materials. It lists the company's address and contact information. It also lists various steel grades and alloys that the company supplies, including stainless steel, carbon steel, alloy steel, and high nickel alloys in the form of sheets, plates, coils, pipes, tubes, and fittings.
This document contains information about Heubach International, an ISO 9001:2008 certified company that exports, stocks, and supplies various ferrous and non-ferrous raw materials. It lists the company's address and contact information. It also lists various steel grades and alloys that the company supplies, including stainless steel, carbon steel, alloy steel, and high nickel alloys in the form of sheets, plates, coils, pipes, tubes, and fittings.
This document provides information on various precision planetary gear reducers and economy gear reducers produced by Zipp Precision Gear Reducer. It includes technical specifications, dimensions, features, and applications for different models of precision planetary gear reducers and economy gear reducers. Product lines covered include PS, PN, RL, RS, ZSR precision planetary gear reducers and EL economy gear reducers. Specifications provided include torque ratings, speed ratios, dimensions, mounting instructions, and more. The document serves as a catalog and reference for selecting the appropriate Zipp precision or economy gear reducer for different applications.
This document appears to be a catalog for precision planetary gear reducers and related products from Zipp Precision Gear Reducer. It includes technical specifications, dimensions, features and applications for their various gear reducer models including precision planetary, precision & economy planetary, and economy gear reducers. Product information is provided for multiple frame sizes with details on torque ratings, speeds, materials, seals, lubrication and other specifications. Mounting instructions and diagrams of dimensional profiles are also included.
The document describes the XTRUE series of precision planetary gearheads. Key details include:
- The gearheads offer benefits like improved load capacity, lower backlash, quieter operation, flexible mounting, and easy replacement.
- They are available in 5 frame sizes with torque capacities up to 876 Nm and ratio ranges from 3:1 to 100:1.
- Specifications include dimensions, specifications, performance specifications, and torque/speed ratings for each model.
Unique to the aviation industry, aviation expert, Stuart Rubin, discusses the ICF Residual Value Model and how it compares to current methodologies in the industry.
This presentation was originally shared at the Air Transportation Research International Forum (ATRIF) on October 21, 2015.
To learn more, visit: http://www.icfi.com/markets/aviation
The APCO Geopolitical Radar - Q3 2024 The Global Operating Environment for Bu...APCO
The Radar reflects input from APCO’s teams located around the world. It distils a host of interconnected events and trends into insights to inform operational and strategic decisions. Issues covered in this edition include:
Storytelling is an incredibly valuable tool to share data and information. To get the most impact from stories there are a number of key ingredients. These are based on science and human nature. Using these elements in a story you can deliver information impactfully, ensure action and drive change.
Best practices for project execution and deliveryCLIVE MINCHIN
A select set of project management best practices to keep your project on-track, on-cost and aligned to scope. Many firms have don't have the necessary skills, diligence, methods and oversight of their projects; this leads to slippage, higher costs and longer timeframes. Often firms have a history of projects that simply failed to move the needle. These best practices will help your firm avoid these pitfalls but they require fortitude to apply.
[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
Key highlights include Microsoft's Digital Transformation Framework, which focuses on driving innovation and efficiency, and McKinsey's Ten Guiding Principles, which provide strategic insights for successful digital transformation. Additionally, Forrester's framework emphasizes enhancing customer experiences and modernizing IT infrastructure, while IDC's MaturityScape helps assess and develop organizational digital maturity. MIT's framework explores cutting-edge strategies for achieving digital success.
These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
Deloitte’s Digital Industrial Transformation Framework
Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
DXC Technology’s Digital Transformation Framework
The BCG Strategy Palette
McKinsey’s Digital Transformation Framework
Digital Transformation Compass
Four Levels of Digital Maturity
Design Thinking Framework
Business Model Canvas
Customer Journey Map
Unveiling the Dynamic Personalities, Key Dates, and Horoscope Insights: Gemin...my Pandit
Explore the fascinating world of the Gemini Zodiac Sign. Discover the unique personality traits, key dates, and horoscope insights of Gemini individuals. Learn how their sociable, communicative nature and boundless curiosity make them the dynamic explorers of the zodiac. Dive into the duality of the Gemini sign and understand their intellectual and adventurous spirit.
Discover innovative uses of Revit in urban planning and design, enhancing city landscapes with advanced architectural solutions. Understand how architectural firms are using Revit to transform how processes and outcomes within urban planning and design fields look. They are supplementing work and putting in value through speed and imagination that the architects and planners are placing into composing progressive urban areas that are not only colorful but also pragmatic.
Part 2 Deep Dive: Navigating the 2024 Slowdownjeffkluth1
Introduction
The global retail industry has weathered numerous storms, with the financial crisis of 2008 serving as a poignant reminder of the sector's resilience and adaptability. However, as we navigate the complex landscape of 2024, retailers face a unique set of challenges that demand innovative strategies and a fundamental shift in mindset. This white paper contrasts the impact of the 2008 recession on the retail sector with the current headwinds retailers are grappling with, while offering a comprehensive roadmap for success in this new paradigm.
HR search is critical to a company's success because it ensures the correct people are in place. HR search integrates workforce capabilities with company goals by painstakingly identifying, screening, and employing qualified candidates, supporting innovation, productivity, and growth. Efficient talent acquisition improves teamwork while encouraging collaboration. Also, it reduces turnover, saves money, and ensures consistency. Furthermore, HR search discovers and develops leadership potential, resulting in a strong pipeline of future leaders. Finally, this strategic approach to recruitment enables businesses to respond to market changes, beat competitors, and achieve long-term success.
Starting a business is like embarking on an unpredictable adventure. It’s a journey filled with highs and lows, victories and defeats. But what if I told you that those setbacks and failures could be the very stepping stones that lead you to fortune? Let’s explore how resilience, adaptability, and strategic thinking can transform adversity into opportunity.
The Most Inspiring Entrepreneurs to Follow in 2024.pdfthesiliconleaders
In a world where the potential of youth innovation remains vastly untouched, there emerges a guiding light in the form of Norm Goldstein, the Founder and CEO of EduNetwork Partners. His dedication to this cause has earned him recognition as a Congressional Leadership Award recipient.
Call8328958814 satta matka Kalyan result satta guessing➑➌➋➑➒➎➑➑➊➍
Satta Matka Kalyan Main Mumbai Fastest Results
Satta Matka ❋ Sattamatka ❋ New Mumbai Ratan Satta Matka ❋ Fast Matka ❋ Milan Market ❋ Kalyan Matka Results ❋ Satta Game ❋ Matka Game ❋ Satta Matka ❋ Kalyan Satta Matka ❋ Mumbai Main ❋ Online Matka Results ❋ Satta Matka Tips ❋ Milan Chart ❋ Satta Matka Boss❋ New Star Day ❋ Satta King ❋ Live Satta Matka Results ❋ Satta Matka Company ❋ Indian Matka ❋ Satta Matka 143❋ Kalyan Night Matka..
Cover Story - China's Investment Leader - Dr. Alyce SUmsthrill
In World Expo 2010 Shanghai – the most visited Expo in the World History
https://www.britannica.com/event/Expo-Shanghai-2010
China’s official organizer of the Expo, CCPIT (China Council for the Promotion of International Trade https://en.ccpit.org/) has chosen Dr. Alyce Su as the Cover Person with Cover Story, in the Expo’s official magazine distributed throughout the Expo, showcasing China’s New Generation of Leaders to the World.
This document contains information about Heubach International, an ISO 9001:2008 certified company that exports, stocks, and supplies various ferrous and non-ferrous raw materials. It lists the company's address and contact information. It also lists various steel grades and alloys that the company supplies, including stainless steel, carbon steel, alloy steel, and high nickel alloys in the form of sheets, plates, coils, pipes, tubes, and fittings.
This document contains information about Heubach International, an ISO 9001:2008 certified company that exports, stocks, and supplies various ferrous and non-ferrous raw materials. It lists the company's address and contact information. It also lists various steel grades and alloys that the company supplies, including stainless steel, carbon steel, alloy steel, and high nickel alloys in the form of sheets, plates, coils, pipes, tubes, and fittings.
This document provides information on various precision planetary gear reducers and economy gear reducers produced by Zipp Precision Gear Reducer. It includes technical specifications, dimensions, features, and applications for different models of precision planetary gear reducers and economy gear reducers. Product lines covered include PS, PN, RL, RS, ZSR precision planetary gear reducers and EL economy gear reducers. Specifications provided include torque ratings, speed ratios, dimensions, mounting instructions, and more. The document serves as a catalog and reference for selecting the appropriate Zipp precision or economy gear reducer for different applications.
This document appears to be a catalog for precision planetary gear reducers and related products from Zipp Precision Gear Reducer. It includes technical specifications, dimensions, features and applications for their various gear reducer models including precision planetary, precision & economy planetary, and economy gear reducers. Product information is provided for multiple frame sizes with details on torque ratings, speeds, materials, seals, lubrication and other specifications. Mounting instructions and diagrams of dimensional profiles are also included.
The document describes the XTRUE series of precision planetary gearheads. Key details include:
- The gearheads offer benefits like improved load capacity, lower backlash, quieter operation, flexible mounting, and easy replacement.
- They are available in 5 frame sizes with torque capacities up to 876 Nm and ratio ranges from 3:1 to 100:1.
- Specifications include dimensions, specifications, performance specifications, and torque/speed ratings for each model.
Unique to the aviation industry, aviation expert, Stuart Rubin, discusses the ICF Residual Value Model and how it compares to current methodologies in the industry.
This presentation was originally shared at the Air Transportation Research International Forum (ATRIF) on October 21, 2015.
To learn more, visit: http://www.icfi.com/markets/aviation
The APCO Geopolitical Radar - Q3 2024 The Global Operating Environment for Bu...APCO
The Radar reflects input from APCO’s teams located around the world. It distils a host of interconnected events and trends into insights to inform operational and strategic decisions. Issues covered in this edition include:
Storytelling is an incredibly valuable tool to share data and information. To get the most impact from stories there are a number of key ingredients. These are based on science and human nature. Using these elements in a story you can deliver information impactfully, ensure action and drive change.
Best practices for project execution and deliveryCLIVE MINCHIN
A select set of project management best practices to keep your project on-track, on-cost and aligned to scope. Many firms have don't have the necessary skills, diligence, methods and oversight of their projects; this leads to slippage, higher costs and longer timeframes. Often firms have a history of projects that simply failed to move the needle. These best practices will help your firm avoid these pitfalls but they require fortitude to apply.
[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
Key highlights include Microsoft's Digital Transformation Framework, which focuses on driving innovation and efficiency, and McKinsey's Ten Guiding Principles, which provide strategic insights for successful digital transformation. Additionally, Forrester's framework emphasizes enhancing customer experiences and modernizing IT infrastructure, while IDC's MaturityScape helps assess and develop organizational digital maturity. MIT's framework explores cutting-edge strategies for achieving digital success.
These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
Deloitte’s Digital Industrial Transformation Framework
Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
DXC Technology’s Digital Transformation Framework
The BCG Strategy Palette
McKinsey’s Digital Transformation Framework
Digital Transformation Compass
Four Levels of Digital Maturity
Design Thinking Framework
Business Model Canvas
Customer Journey Map
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Explore the fascinating world of the Gemini Zodiac Sign. Discover the unique personality traits, key dates, and horoscope insights of Gemini individuals. Learn how their sociable, communicative nature and boundless curiosity make them the dynamic explorers of the zodiac. Dive into the duality of the Gemini sign and understand their intellectual and adventurous spirit.
Discover innovative uses of Revit in urban planning and design, enhancing city landscapes with advanced architectural solutions. Understand how architectural firms are using Revit to transform how processes and outcomes within urban planning and design fields look. They are supplementing work and putting in value through speed and imagination that the architects and planners are placing into composing progressive urban areas that are not only colorful but also pragmatic.
Part 2 Deep Dive: Navigating the 2024 Slowdownjeffkluth1
Introduction
The global retail industry has weathered numerous storms, with the financial crisis of 2008 serving as a poignant reminder of the sector's resilience and adaptability. However, as we navigate the complex landscape of 2024, retailers face a unique set of challenges that demand innovative strategies and a fundamental shift in mindset. This white paper contrasts the impact of the 2008 recession on the retail sector with the current headwinds retailers are grappling with, while offering a comprehensive roadmap for success in this new paradigm.
HR search is critical to a company's success because it ensures the correct people are in place. HR search integrates workforce capabilities with company goals by painstakingly identifying, screening, and employing qualified candidates, supporting innovation, productivity, and growth. Efficient talent acquisition improves teamwork while encouraging collaboration. Also, it reduces turnover, saves money, and ensures consistency. Furthermore, HR search discovers and develops leadership potential, resulting in a strong pipeline of future leaders. Finally, this strategic approach to recruitment enables businesses to respond to market changes, beat competitors, and achieve long-term success.
Starting a business is like embarking on an unpredictable adventure. It’s a journey filled with highs and lows, victories and defeats. But what if I told you that those setbacks and failures could be the very stepping stones that lead you to fortune? Let’s explore how resilience, adaptability, and strategic thinking can transform adversity into opportunity.
The Most Inspiring Entrepreneurs to Follow in 2024.pdfthesiliconleaders
In a world where the potential of youth innovation remains vastly untouched, there emerges a guiding light in the form of Norm Goldstein, the Founder and CEO of EduNetwork Partners. His dedication to this cause has earned him recognition as a Congressional Leadership Award recipient.
Call8328958814 satta matka Kalyan result satta guessing➑➌➋➑➒➎➑➑➊➍
Satta Matka Kalyan Main Mumbai Fastest Results
Satta Matka ❋ Sattamatka ❋ New Mumbai Ratan Satta Matka ❋ Fast Matka ❋ Milan Market ❋ Kalyan Matka Results ❋ Satta Game ❋ Matka Game ❋ Satta Matka ❋ Kalyan Satta Matka ❋ Mumbai Main ❋ Online Matka Results ❋ Satta Matka Tips ❋ Milan Chart ❋ Satta Matka Boss❋ New Star Day ❋ Satta King ❋ Live Satta Matka Results ❋ Satta Matka Company ❋ Indian Matka ❋ Satta Matka 143❋ Kalyan Night Matka..
Cover Story - China's Investment Leader - Dr. Alyce SUmsthrill
In World Expo 2010 Shanghai – the most visited Expo in the World History
https://www.britannica.com/event/Expo-Shanghai-2010
China’s official organizer of the Expo, CCPIT (China Council for the Promotion of International Trade https://en.ccpit.org/) has chosen Dr. Alyce Su as the Cover Person with Cover Story, in the Expo’s official magazine distributed throughout the Expo, showcasing China’s New Generation of Leaders to the World.
The Genesis of BriansClub.cm Famous Dark WEb PlatformSabaaSudozai
BriansClub.cm, a famous platform on the dark web, has become one of the most infamous carding marketplaces, specializing in the sale of stolen credit card data.
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This letter, written by Kellen Harkins, Course Director at Full Sail University, commends Anny Love's exemplary performance in the Video Sharing Platforms class. It highlights her dedication, willingness to challenge herself, and exceptional skills in production, editing, and marketing across various video platforms like YouTube, TikTok, and Instagram.
Negotiation & Presentation Skills regarding steps in business communication, ...
FBS-Group-A06-Project-Report.pdf
1. The automobile data analysis includes a dataset introduced from the Univerisity of California Irvine
Machine Learning Repository UCI. According to UCI (1985), the attributes consist of three different
types of entities: (a) the model and specification of an auto, which includes the characteristics, (b) the
personal insurance, (c) its normalized losses in use as compared to other cars. The data set source for
this model collected from Insurance collision reports, personal insurance, and car models. There are
26 data attributes in this model descript the data set model from different angles. The objective of this
report is to perform exploratory data analysis to find the primary relationships between features,
which include univariate analysis, which includes finding the maximum and minimum, such as the
weight, engine size, horsepower, and price. Moreover, perform a regression to predict the car prices.
AUTOMOBILE DATA ANALYSIS
Sri Mounica Kalidasu,Anand Desika, Saketh GV, Praveen Kumar A, Marcel Tino
EXPLORATORY ANALYSIS
Importing Libraries and Setting up Input Path
In [1]: #Importing Libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Input data files from specified paths
import warnings
warnings.filterwarnings("ignore")
import os
print(os.listdir("C:/Users/kalid/Downloads/input"))
#Input Excel File
df_automobile = pd.read_csv("C:/Users/kalid/Downloads/input/Automobile_da
ta.csv")
Data Cleaning
Data contains "?" replace it with NAN
['Automobile_data.csv']
7. Fill missing data of normalised-losses, price, horsepower, peak-rpm, bore, stroke with the
respective column mean
Fill missing data category Number of doors with the mode of the column
8. In [4]: df_temp = df_automobile[df_automobile[' normalized-losses']!='?']
normalised_mean = df_temp[' normalized-losses'].astype(int).mean()
df_automobile[' normalized-losses'] = df_automobile[' normalized-losses']
.replace('?',normalised_mean).astype(int)
df_temp = df_automobile[df_automobile[' price']!='?']
normalised_mean = df_temp[' price'].astype(int).mean()
df_automobile[' price'] = df_automobile[' price'].replace('?',normalised_
mean).astype(int)
df_temp = df_automobile[df_automobile[' horsepower']!='?']
normalised_mean = df_temp[' horsepower'].astype(int).mean()
df_automobile[' horsepower'] = df_automobile[' horsepower'].replace('?',n
ormalised_mean).astype(int)
df_temp = df_automobile[df_automobile[' peak-rpm']!='?']
normalised_mean = df_temp[' peak-rpm'].astype(int).mean()
df_automobile[' peak-rpm'] = df_automobile[' peak-rpm'].replace('?',norma
lised_mean).astype(int)
df_temp = df_automobile[df_automobile[' bore']!='?']
normalised_mean = df_temp[' bore'].astype(float).mean()
df_automobile[' bore'] = df_automobile[' bore'].replace('?',normalised_me
an).astype(float)
df_temp = df_automobile[df_automobile[' stroke']!='?']
normalised_mean = df_temp[' stroke'].astype(float).mean()
df_automobile[' stroke'] = df_automobile[' stroke'].replace('?',normalise
d_mean).astype(float)
df_automobile[' num-of-doors'] = df_automobile[' num-of-doors'].replace(
'?','four')
df_automobile.head()
Summary statistics of variable
Out[4]:
symboling
normalized-
losses
make
fuel-
type
aspiration
num-
of-
doors
body-
style
drive-
wheels
eng
locat
0 3 122
alfa-
romero
gas std two convertible rwd f
1 3 122
alfa-
romero
gas std two convertible rwd f
2 1 122
alfa-
romero
gas std two hatchback rwd f
3 2 164 audi gas std four sedan fwd f
4 2 164 audi gas std four sedan 4wd f
5 rows × 26 columns
10. In [6]: def scatter(x,fig):
plt.subplot(5,2,fig)
plt.scatter(df_automobile[x],df_automobile[' price'])
plt.title(x+' vs Price')
plt.ylabel('Price')
plt.xlabel(x)
plt.figure(figsize=(10,20))
scatter(' length', 1)
scatter(' width', 2)
scatter(' height', 3)
scatter(' curb-weight', 4)
plt.tight_layout()
Findings
width, length and curbweight seems to have a poitive correlation with price.
height does not show any significant trend with price.
Univariate Analysis
11. In [7]: # 1 plt.figure(figsize=(10,8))
df_automobile[[' engine-size',' peak-rpm',' curb-weight',' compression-ra
tio',' horsepower',' price']].hist(figsize=(10,8),bins=6,color='Y')
# 2 plt.figure(figsize=(10,8))
plt.tight_layout()
plt.show()
Findings
Compression Ratio of the cars is in a range of 5 to 13
Most of the car has a Curb Weight is in range 1900 to 3100
The Engine Size is inrange 60 to 190
Most vehicle has horsepower 50 to 125
Most Vehicle are in price range 5000 to 18000
peak rpm is mostly distributed between 4600 to 5700
12. In [8]: plt.figure(1)
plt.subplot(221)
df_automobile[' engine-type'].value_counts(normalize=True).plot(figsize=(
10,8),kind='bar',color='red')
plt.title("Number of Engine Type frequency diagram")
plt.ylabel('Number of Engine Type')
plt.xlabel(' engine-type');
plt.subplot(222)
df_automobile[' num-of-doors'].value_counts(normalize=True).plot(figsize=
(10,8),kind='bar',color='green')
plt.title("Number of Door frequency diagram")
plt.ylabel('Number of Doors')
plt.xlabel(' num-of-doors');
plt.subplot(223)
df_automobile[' fuel-type'].value_counts(normalize= True).plot(figsize=(1
0,8),kind='bar',color='purple')
plt.title("Number of Fuel Type frequency diagram")
plt.ylabel('Number of vehicles')
plt.xlabel(' fuel-type');
plt.subplot(224)
df_automobile[' body-style'].value_counts(normalize=True).plot(figsize=(1
0,8),kind='bar',color='orange')
plt.title("Number of Body Style frequency diagram")
plt.ylabel('Number of vehicles')
plt.xlabel(' body-style');
plt.tight_layout()
plt.show()
13. Findings
More than 70 % of the vehicle has Ohc type of Engine
57% of the cars has 4 doors
Gas is preferred by 85 % of the vehicles
Most produced vehicle are of body style sedan around 48% followed by hatchback 32%
14. In [9]: import seaborn as sns
corr = df_automobile.corr()
plt.figure(figsize=(20,9))
a = sns.heatmap(corr, annot=True, fmt='.2f')
Findings
curb-size, engine-size, horsepower are positively corelated
city-mpg,highway-mpg are negatively corelate
Bivariate Analysis (PRICE ANALYSIS)
15. In [10]: plt.figure(figsize=(25, 6))
df = pd.DataFrame(df_automobile.groupby([' make'])[' price'].mean().sort_
values(ascending = False))
df.plot.bar()
plt.title('Company Name vs Average Price')
plt.show()
df = pd.DataFrame(df_automobile.groupby([' fuel-type'])[' price'].mean().
sort_values(ascending = False))
df.plot.bar()
plt.title('Fuel Type vs Average Price')
plt.show()
df = pd.DataFrame(df_automobile.groupby([' body-style'])[' price'].mean()
.sort_values(ascending = False))
df.plot.bar()
plt.title(' body-style vs Average Price')
plt.show()
17. Findings
Jaguar and Buick seem to have highest average price.
diesel has higher average price than gas.
hardtop and convertible have higher average price.
In [11]: plt.rcParams['figure.figsize']=(20,6)
ax = sns.boxplot(x=" make", y=" price", data=df_automobile)
18. In [12]: sns.catplot(data=df_automobile, x=" body-style", y=" price", hue=" aspira
tion" ,kind="point")
In [13]: plt.rcParams['figure.figsize']=(8,3)
ax = sns.boxplot(x=" drive-wheels", y=" price", data=df_automobile)
Out[12]: <seaborn.axisgrid.FacetGrid at 0x24a1c094a88>
19. Findings
Mercedez-Benz ,BMW, Jaguar, Porshe produces expensive cars more than 25000
cheverolet,dodge, honda,mitbushi, nissan,plymouth subaru,toyata produces budget models
with lower prices
most of the cars comapany produces car in range below 25000
Hardtop model are expensive in prices followed by convertible and sedan body style
Turbo models have higher prices than for the standard model
Convertible has only standard edition with expensive cars
hatchback and sedan turbo models are available below 20000
rwd wheel drive vehicle have expensive prices
In [14]: plt.rcParams['figure.figsize']=(8,3)
ax = sns.boxenplot(x=" engine-type", y=" price", data=df_automobile)
In [15]: plt.rcParams['figure.figsize']=(8,3)
ay = sns.swarmplot(x=" engine-type", y=" engine-size", data=df_automobil
e)
20. In [16]: sns.catplot(data=df_automobile, x=" num-of-cylinders", y=" horsepower")
Findings
ohc is the most used Engine Type both for diesel and gas
Diesel vehicle have Engine type "ohc" and "I" and engine size ranges between 100 to 190
Engine type ohcv has the bigger Engine size ranging from 155 to 300
Body-style Hatchback uses max variety of Engine Type followed by sedan
Body-style Convertible is not available with Diesel Engine type
Vehicle with above 200 horsepower has Eight Twelve Six cyclinders
Out[16]: <seaborn.axisgrid.FacetGrid at 0x24a1bf3e148>
21. In [17]: sns.catplot(data=df_automobile, y=" normalized-losses", x=" symboling" ,
hue=" body-style" ,kind="point")
Losses Findings
Note :- here +3 means risky vehicle and -2 means safe vehicle
Increased in risk rating linearly increases in normalised losses in vehicle
covertible car and hardtop car has mostly losses with risk rating above 0
hatchback cars has highest losses at risk rating 3
sedan and Wagon car has losses even in less risk (safe)rating
Out[17]: <seaborn.axisgrid.FacetGrid at 0x24a1bf84908>
22. In [18]: g = sns.pairplot(df_automobile[[" city-mpg", " horsepower", " engine-siz
e", " curb-weight"," price", " fuel-type"]], hue=" fuel-type", diag_kind=
"hist")
Findings
Vehicle Mileage decrease as increase in Horsepower , engine-size, Curb Weight
As horsepower increase the engine size increases
Curbweight increases with the increase in Engine Size
Price Analysis
engine size and curb-weight is positively co realted with price
city-mpg is negatively corelated with price as increase horsepower reduces the mileage
Deriving new features
23. Fuel economy and Cars Range
In [19]: df_automobile[' fueleconomy'] = (0.55 * df_automobile[' city-mpg']) + (0.
45 * df_automobile[' highway-mpg'])
In [20]: #Binning the Car Companies based on avg prices of each Company.
df_automobile[' price'] = df_automobile[' price'].astype('int')
temp = df_automobile.copy()
table = temp.groupby([' make'])[' price'].mean()
temp = temp.merge(table.reset_index(), how='left',on=' make')
bins = [0,10000,20000,40000]
cars_bin=['Budget','Medium','Highend']
df_automobile['carsrange'] = pd.cut(temp[' price_y'],bins,right=False,lab
els=cars_bin)
df_automobile.head()
Out[20]:
symboling
normalized-
losses
make
fuel-
type
aspiration
num-
of-
doors
body-
style
drive-
wheels
eng
locat
0 3 122
alfa-
romero
gas std two convertible rwd f
1 3 122
alfa-
romero
gas std two convertible rwd f
2 1 122
alfa-
romero
gas std two hatchback rwd f
3 2 164 audi gas std four sedan fwd f
4 2 164 audi gas std four sedan 4wd f
5 rows × 28 columns
24. In [21]: plt.figure(figsize=(25, 6))
df = pd.DataFrame(df_automobile.groupby([' fuel-system',' drive-wheels',
'carsrange'])[' price'].mean().unstack(fill_value=0))
df.plot.bar()
plt.title('Car Range vs Average Price')
plt.show()
In [22]: df_automobile_lr = df_automobile[[' price', ' fuel-type', ' aspiration','
body-style',' drive-wheels',' wheel-base',
' curb-weight', ' engine-type', ' num-of-cylinders', '
engine-size', ' bore',' horsepower',' fueleconomy',
' height', ' length',' width', 'carsrange']]
df_automobile_lr.head()
<Figure size 1800x432 with 0 Axes>
Out[22]:
price
fuel-
type
aspiration
body-
style
drive-
wheels
wheel-
base
curb-
weight
engine-
type
num-of-
cylinders
e
0 13495 gas std convertible rwd 88.6 2548 dohc four
1 16500 gas std convertible rwd 88.6 2548 dohc four
2 16500 gas std hatchback rwd 94.5 2823 ohcv six
3 13950 gas std sedan fwd 99.8 2337 ohc four
4 17450 gas std sedan 4wd 99.4 2824 ohc five
28. In [29]: #Correlation using heatmap
plt.figure(figsize = (30, 25))
sns.heatmap(df_train.corr(), annot = True, cmap="YlGnBu")
plt.show()
Highly correlated variables to price are - curbweight , enginesize , horsepower , carwidth
and highend .
In [30]: #Dividing data into X and y variables
y_train = df_train.pop(' price')
X_train = df_train
Model Building
Recursive Feature Elimination (RFE) is popular because it is easy to configure and use and
because it is effective at selecting those features (columns) in a training dataset that are more
or most relevant in predicting the target variable.
30. In [33]: X_train_rfe = X_train[X_train.columns[rfe.support_]]
X_train_rfe.head()
In [34]: def build_model(X,y):
X = sm.add_constant(X) #Adding the constant
lm = sm.OLS(y,X).fit() # fitting the model
print(lm.summary()) # model summary
return X
def checkVIF(X):
vif = pd.DataFrame()
vif['Features'] = X.columns
vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X
.shape[1])]
vif['VIF'] = round(vif['VIF'], 2)
vif = vif.sort_values(by = "VIF", ascending = False)
return(vif)
There are some guidelines we can use to determine whether our VIFs are in an acceptable
range. A rule of thumb commonly used in practice is if a VIF is > 10, you have high
multicollinearity.
MODEL1
Out[33]:
curb-
weight
horsepower fueleconomy width hatchback sedan wagon doh
122 0.272692 0.083333 0.530864 0.291667 0 1 0
125 0.500388 0.395833 0.213992 0.666667 1 0 0
166 0.314973 0.266667 0.344307 0.308333 1 0 0
1 0.411171 0.262500 0.244170 0.316667 0 0 0
199 0.647401 0.475000 0.122085 0.575000 0 0 1
33. p-vale of twelve seems to be higher than the significance value of 0.05, hence dropping it as
it is insignificant in presence of other variables.
In [36]: X_train_new = X_train_rfe.drop(["twelve"], axis = 1)
MODEL2
=========================================================================
=====
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is co
rrectly specified.
36. In [39]: X_train_new = X_train_new.drop([" fueleconomy"], axis = 1)
MODEL 3
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is co
rrectly specified.
39. In [41]: #Calculating the Variance Inflation Factor
checkVIF(X_train_new)
dropping curbweight because of high VIF value. (shows that curbweight has high
multicollinearity.)
In [43]: X_train_new = X_train_new.drop([" curb-weight"], axis = 1)
MODEL 4
[1] Standard Errors assume that the covariance matrix of the errors is co
rrectly specified.
Out[41]:
Features VIF
0 const 26.88
1 curb-weight 8.04
5 sedan 6.08
4 hatchback 5.63
3 width 5.13
2 horsepower 3.59
6 wagon 3.56
8 Highend 1.63
7 dohcv 1.45
47. In [51]: checkVIF(X_train_new)
MODEL 7
Out[51]:
Features VIF
0 const 10.40
1 horsepower 2.40
2 width 2.10
5 Highend 1.53
4 dohcv 1.21
3 hatchback 1.13
48. In [52]: #Dropping dohcv to see the changes in model statistics
X_train_new = X_train_new.drop(["dohcv"], axis = 1)
X_train_new = build_model(X_train_new,y_train)
checkVIF(X_train_new)
50. Residual Analysis of Model
In [53]: lm = sm.OLS(y_train,X_train_new).fit()
y_train_price = lm.predict(X_train_new)
In [54]: # Plot the histogram of the error terms
fig = plt.figure()
sns.distplot((y_train - y_train_price), bins = 20)
fig.suptitle('Error Terms', fontsize = 20) # Plot headin
g
plt.xlabel('Errors', fontsize = 18)
Error terms seem to be approximately normally distributed, so the assumption on the linear
modeling seems to be fulfilled.
Prediction and Evaluation
In [56]: #Scaling the test set
num_vars = [' wheel-base', ' curb-weight', ' engine-size', ' bore', ' hor
sepower',' fueleconomy',' length',' width',' price']
df_test[num_vars] = scaler.fit_transform(df_test[num_vars])
Out[52]:
Features VIF
0 const 10.05
1 horsepower 2.23
2 width 2.10
4 Highend 1.53
3 hatchback 1.11
Out[54]: Text(0.5, 0, 'Errors')
51. In [57]: #Dividing into X and y
y_test = df_test.pop(' price')
X_test = df_test
In [58]: # Now let's use our model to make predictions.
X_train_new = X_train_new.drop('const',axis=1)
# Creating X_test_new dataframe by dropping variables from X_test
X_test_new = X_test[X_train_new.columns]
# Adding a constant variable
X_test_new = sm.add_constant(X_test_new)
In [59]: # Making predictions
y_pred = lm.predict(X_test_new)
Evaluation of test via comparison of y_pred and
y_test
In [60]: from sklearn.metrics import r2_score
r2_score(y_test, y_pred)
In [61]: #EVALUATION OF THE MODEL
# Plotting y_test and y_pred to understand the spread.
fig = plt.figure()
plt.scatter(y_test,y_pred)
fig.suptitle('y_test vs y_pred', fontsize=20) # Plot heading
plt.xlabel('y_test', fontsize=18) # X-label
plt.ylabel('y_pred', fontsize=16)
Evaluation of the model using Statistics
Out[60]: 0.9037424400203523
Out[61]: Text(0, 0.5, 'y_pred')
53. Findings
R-sqaured and Adjusted R-squared (extent of fit) - 0.899 and 0.896 - 90% variance explained.
F-stats and Prob(F-stats) (overall model fit) - 308.0 and 1.04e-67(approx. 0.0) - Model fir is
significant and explained 90% variance is just not by chance.
p-values - p-values for all the coefficients seem to be less than the significance level of 0.05. -
meaning that all the predictors are statistically significant.
In [64]: import sklearn.metrics as sm
print("Mean absolute error =", round(sm.mean_absolute_error(y_test, y_pre
d), 5))
#print("Root Mean Squared Error =", round(sm.sqrt(mean_squared_error(y_te
st, y_pred)),5))
print("Mean squared error =", round(sm.mean_squared_error(y_test, y_pred
), 5))
print("Median absolute error =", round(sm.median_absolute_error(y_test, y
_pred), 5))
print("Explain variance score =", round(sm.explained_variance_score(y_tes
t, y_pred), 5))
print("R2 score =", round(sm.r2_score(y_test, y_pred), 5))
Mean absolute error: This is the average of absolute errors of all the data points in the given
dataset.
Mean squared error: This is the average of the squares of the errors of all the data points in
the given dataset. It is one of the most popular metrics out there!
Median absolute error: This is the median of all the errors in the given dataset. The main
advantage of this metric is that it's robust to outliers. A single bad point in the test dataset
wouldn't skew the entire error metric, as opposed to a mean error metric.
Explained variance score: This score measures how well our model can account for the
variation in our dataset. A score of 1.0 indicates that our model is perfect.
R2 score: This is pronounced as R-squared, and this score refers to the coefficient of
determination. This tells us how well the unknown samples will be predicted by our model. The
best possible score is 1.0, but the score can be negative as well.
A good practice is to make sure that the mean squared error is low and the explained variance
score is high.
Mean absolute error = 0.05202
Mean squared error = 0.00421
Median absolute error = 0.04143
Explain variance score = 0.90954
R2 score = 0.90374