Upcoming SlideShare
×

# Data Mining

• 2,102 views

Study research work

Study research work

More in: Education
• Comment goes here.
Are you sure you want to
• @abhineet @kawaldeep - thanks
Are you sure you want to
• yeah i know :) althought i dint get a bit of it :P but ya have made it so its ROCKING :)
Are you sure you want to
• dis 1 wil definitely win...
Are you sure you want to

Total Views
2,102
On Slideshare
0
From Embeds
0
Number of Embeds
1

Shares
51
3
Likes
1

No embeds

### Report content

No notes for slide

### Transcript

• 1. Data Mining –A Tool For Decision Makers THROUGH DECSION TREE TECHNIQUE
• 2. AN INTRODUCTION
• Increasing competition- “critical decision”
• Accurate decisions :-
• Experience
• Dataware-house and data-mining
• Dataware-house
• Data-mining
• 3. Various Techniques of Data-mining
• Decision Tree
• Rule Induction
• Nearest Neighbors
• Clustering
• Genetic Algorithm
• Exploratory analysis
• 4. Introduction To Decision Tree Technique
• Decision tree uses a graph/tree based model and helps in making decisions, that is, reaching the destination with the best possible strategy.
• Decision trees are powerful and popular tools for classification and prediction.
• It represents rules
• Human understandable.
• Accessible in languages like SQL
• Used to identify the strategy most likely to reach a goal.
• means for calculating conditional probabilities
• It is a predictive model (a mapping from observations about an item to conclusions about its target value)
• 5. Key Requirements For Decision Tree
• Attribute-value description
• Predefined classes (target attribute values)
• Discrete classes
• Sufficient data
• 6. Strengths Of Decision Tree Technique
• Decision trees are able to generate understandable rules.
• Decision trees perform classification without requiring much computation.
• Decision trees are able to handle both continuous and categorical variables.
• Decision trees provide a clear indication of which fields are most important for prediction or classification.
• 7. EXAMPLE 1:MUSIC STORE AND SALES Low Hit Hindi Movie Low Average English Songs Low Hit Religious Songs Low Hit Hindi Movie High Hit Hindi Movie Low Average English Movie High Average Hindi Songs High Hit English Songs High Hit English Songs High Average Hindi Songs High Hit Religious Movie Low Flop English Movie Low Hit Hindi Movie High Hit Hindi Movie Low Hit English Movie Low Average English Songs High Hit Religious Songs High Flop Hindi Songs High Hit Hindi Movie Sales On Charts Type CDs
• 8. Decision Tree Of Example 1 TYPE HINDI (CD)(6/9) RELIGIOUS (ON CHARTS)(3/3) ENGLISH (CD)(4/7) HIT MOVIE (ON CHARTS) (6/6) SONG (SALES)(3/3) SONGS (ON CHARTS)(2/4) MOVIE (SALES)(1/1) HIT HIGH HIT (SALSES)(1/1) AVERAGE (SALES)(1/1) HIGH LOW LOW ROOT NODE LEAF NODE
• 9. NOTE :-
• All nodes except the leaf nodes can split. Leaf nodes represent perfect subsets.
• Now we can easily predict from the set of rules derived from the decision tree.
• The above decision tree is not the only possibility, we can have more than one decision tree.
• William Occam states that the shortest possible solution should be preferred over the more complex and lengthy ones.
• It is very important to understand that certain attributes should be kept higher in the order than others this helps in making the splitting of the non-leaf nodes in a better format (one is pure).
• 10. ADVANTAGES OF DECISION TREE
• It can be used on any type of data i.e. data can be numeric or informative type.
• It uses a white box technique
• It is applicable to large amount of data too.
• It is a reliable model because we can validate our information statistically.
• Data can be directly used from dataset i.e. no initial or very less initial work has to be done.
• Because it is a graphical approach it is simple to understand and decipher.
• 11. Example 2 :: LOANS IN BANK Yes No No 12000 Middle class Yes Yes No 65000 Rich Yes No Yes 2000 Poor No Yes Yes 10000 Middle class Yes No Yes 200000 Rich No No No 50000 Middle class Yes No Yes 3000 Poor No Yes No 7000 Poor No Yes Yes 50000 Rich Yes No No 40000 Rich Yes No No 20000 Middle class No Yes Yes 5000 Poor No No No 100000 Rich Yes No Yes 25000 Middle class Yes Yes Yes 70000 Rich New Loan Approved Any Previous Due In The Same Bank Bank Acc. In The Same Bank Amount Of Loan (in Rs.) Customer Background
• 12. Decision Tree Of Example 2 TYPE RICH >40000 MIDDLE CLASS 10000<x<50000 POOR <10000 LOAN APPROVED HAS ACC IN THE SAME BANK HAS A DUE WITH BANK LOAN NOT APPROVED DOESN’T HAS A DUE HAS AN ACC IN THE SAME BANK LOAN APPROVED DOESN’T HAS AN ACC LOAN DENIED
• 13. THANKING YOU
• GARIMA SINGH
• KANWALDEEP SINGH
• BARJESH KOCHAR
• DR.RAJENDER SINGH CHHILLAR