Starting from:
$30
AMI23B– Business Intelligence Lab3 Solved
Task1: Descriptive Analysis, Unsupervised Learning – IKEA
This task is about finding and evaluating clusters that contains data with similar properties.
Your task is to discover some new places here in Sweden that may be suitable for IKEA department stores. You will do this by using the kmeans method. To aid you in your findings, you have a text file, ikea_data.txt, which contains important features for many of Sweden’s municipalities. The English term municipality translated to Swedish is kommun.
IKEA stores are already available in the following municipalities:
Borlänge, Gävle, Göteborg, Haparanda, Helsingborg, Jönköping,
Kalmar, Karlstad, Linköping, Malmö, Stockholm, Sundsvall, Uddevalla, Umeå, Uppsala, Västerås, Älmhult, and Örebro. Some of these municipalities are missing in the ikea_data.txt file. The following link
shows a map of Sweden's municipalities,
https://www.scb.se/contentassets/1e02934987424259b730c5e9a82f 7e74/kommunkarta09.pdf
General steps to follow: data exploration, data transformation, data reduction and then implement k-means clustering method.
Task2: Predictive Analysis, Supervised Learning – Titanic
This task is about classifying a large set of data based on a set of preclassified samples.
Your task is to predict whether a passenger survived the Titanic shipwreck of not. You will use a Decision Tree Classifier to do this.
General steps to follow: data exploration and analysis, data preprocessing and transformation (handle missing values, convert categorical features into numeric, convert discrete features into binary etc.), implement your decision tree classifier.
The classic Titanic dataset provides information on the fate of passengers on the Titanic, summarized according to economic status (class), sex, age and survival.
You will find two data files:
• Training set (train.csv), should be used to build your ML models.
• Test set (test.csv), should be used to see how well your model performs on unseen data.
Data Description and Notes:
pclass: A proxy for socio-economic status (SES)
• 1st = Upper
• 2nd = Middle
• 3rd = Lower
age: In years. Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5
sibsp: number of siblings / spouses aboard the Titanic. The dataset defines family relations in this way...
• Sibling = brother, sister, stepbrother, stepsister
• Spouse = husband, wife (mistresses and fiancés were ignored) parch: number of parents / children aboard the Titanic. The dataset defines family relations in this way...
• Parent = mother, father
• Child = daughter, son, stepdaughter, stepson
• Some children travelled only with a nanny, therefore parch=0 for them.
Embarked: Port of Embarkation C = Cherbourg, Q = Queenstown, S = Southampton.
Ticket: Ticket number
Fare: Passenger fare
Cabin: Cabin number
This task is about finding and evaluating clusters that contains data with similar properties.
Your task is to discover some new places here in Sweden that may be suitable for IKEA department stores. You will do this by using the kmeans method. To aid you in your findings, you have a text file, ikea_data.txt, which contains important features for many of Sweden’s municipalities. The English term municipality translated to Swedish is kommun.
IKEA stores are already available in the following municipalities:
Borlänge, Gävle, Göteborg, Haparanda, Helsingborg, Jönköping,
Kalmar, Karlstad, Linköping, Malmö, Stockholm, Sundsvall, Uddevalla, Umeå, Uppsala, Västerås, Älmhult, and Örebro. Some of these municipalities are missing in the ikea_data.txt file. The following link
shows a map of Sweden's municipalities,
https://www.scb.se/contentassets/1e02934987424259b730c5e9a82f 7e74/kommunkarta09.pdf
General steps to follow: data exploration, data transformation, data reduction and then implement k-means clustering method.
Task2: Predictive Analysis, Supervised Learning – Titanic
This task is about classifying a large set of data based on a set of preclassified samples.
Your task is to predict whether a passenger survived the Titanic shipwreck of not. You will use a Decision Tree Classifier to do this.
General steps to follow: data exploration and analysis, data preprocessing and transformation (handle missing values, convert categorical features into numeric, convert discrete features into binary etc.), implement your decision tree classifier.
The classic Titanic dataset provides information on the fate of passengers on the Titanic, summarized according to economic status (class), sex, age and survival.
You will find two data files:
• Training set (train.csv), should be used to build your ML models.
• Test set (test.csv), should be used to see how well your model performs on unseen data.
Data Description and Notes:
pclass: A proxy for socio-economic status (SES)
• 1st = Upper
• 2nd = Middle
• 3rd = Lower
age: In years. Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5
sibsp: number of siblings / spouses aboard the Titanic. The dataset defines family relations in this way...
• Sibling = brother, sister, stepbrother, stepsister
• Spouse = husband, wife (mistresses and fiancés were ignored) parch: number of parents / children aboard the Titanic. The dataset defines family relations in this way...
• Parent = mother, father
• Child = daughter, son, stepdaughter, stepson
• Some children travelled only with a nanny, therefore parch=0 for them.
Embarked: Port of Embarkation C = Cherbourg, Q = Queenstown, S = Southampton.
Ticket: Ticket number
Fare: Passenger fare
Cabin: Cabin number
1 file (338.7KB)
