$$Write $$a $$report $$about $$Data $$Mining $$for $$the $$following $$perspective$.$

database: $$flags

Implementation:

$$ If there are records with missing values, delete them or replace them with average values.

$$ Test and use a feature extraction$/$dimensionality reduction technique $($such as PCA$).$

$$ Choose at least $7$ data mining$/$machine learning techniques for implementation.

$$ Use holdout validation $(80\%$ for training and $\%20$ for test$)$ and $10-$fold cross validation methods

for data dividing.

$$ Compare the performance of chosen data mining$/$machine learning techniques using following

evaluation metrics: classification accuracy, precision, recall $($sensitivity$)$ and F$-$measure $($Fscore$)$

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$$In $$the $$Methodology $$Section

$$In $$the $$Methods $$Subsection$,$briefly $$mention $$about $$chosen $$techniques $$and $$feature extraction$/$dimensionality $$reduction $$technique$.$

$$In $$the $$Data $$Description $$Subsection$,$describe $$the $$selected $$dataset $($including $$# $$records$,$feature $$names$,$feature $$value $$types$,$feature $$value $$ranges$,$and $$# $$missing $$value$).$

$$In $$the $$Evaluation $$Metrics $$Subsection$,$explain $$the $$metrics $$and $$write $$their $$formulas$.$

$$In $$the $$Experimental $$Results $$Section$,$write $$your $$system $$configurations $($including $$operating $$system$,$CPU$,$RAM $$and $$used $$program$)$and $$compare $$the $$performance $$of $$selected $$classifiers $$in $$terms $$of $$these $$metrics $$

$($create $$tables $$for $$comparing$).$

$$ In the Conclusions Section, provide discussion of the overall coverage of the project.