IntroMachineLearning_EnronFraud/Machine_Learning_Final_Project.html at master · rknlhrqy/IntroMachineLearning_EnronFraud · GitHub

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<!DOCTYPE html>
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<title>Machine_Learning_Final_Project</title>

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<h1 id="My-Plan">My Plan<a class="anchor-link" href="#My-Plan">&#182;</a></h1>
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<p>1- During data munging, I remove outlier (only TOTAL removed) and remove missing data (Removed data that did not have financial data). I removed all data that did not have 'salary' information. Rationale for doing so was that a classifer algorithm can identify missing data as a feature itself. As the number of POIs with missing data was minimal (i think 1), I thought it was ok to remove data without financial information. Also if you dont remove financial data, and impute missing values by 0, You will get an algorithm that has high precision, because of all the people in missing data, only 1 person is POI, rest are non-POIs. Do not impute median for missing data. This is not a good strategy for this data set. Reasons below,</p>
<p>1.1- Many employees dont get additional financial incentives like bonus, restricted stock etc, so its not good to put median values for those people, its better to imput 0. Mainly because a lower level employee may actually be getting 0 additional incentives, and that data may not have been included in the data set.</p>
<p>1.2- About 60 data points have email features alone, and do not have financial data. I removed them completely. But putting median values for them may not be correct representation of data. For people with missing financial features, only 1 or 2 are POIs, so including or removing them, only increases or decreases precision and doesn't affect the overall approach in anyway.</p>
<p>I only included data that had entries for salary. The ones that did not have salary information had only email features are removed. There was a lesson about choosing data from different sources, so I excluded ones with email completely, to avoid issues due to data from different sources.</p>
<p>2- Applied log(1+data) to financial information, to get more 'normal' distribution. I computed ratios for email features and added two new email radio features.</p>
<p>3- Did PCA on financial features and selected 3 best. (PCA on training data only). (3- obtained using grid search CV)</p>
<p>3.1- Do not take PCAs on combined financial and email data. This is troublesome due to a lot of missing data, futher a hybrid email and financial data is tough to visualize for me, so I kept them separate.</p>
<p>3.2- I chose 3 PCA components, because they explained 70% of variance in data for the features. 3-4 principal components may be sufficient to capture most of the variance in the data.</p>
<p>3.3- Do not select "total_payments" and "total_stock_incentives", because they are perfectly correlated with other features.</p>
<p>4- The default 0.3 split from sklearn's cross validation function is used. In general smaller test_size implies overfitting. However, we are repeating this for all data points, so we are in a way using all the data. Further, in cases like this where the number of samples is low, using lower split actually avoids overfitting. Because in modeling, we are predicting only 1 data point, so the model has to be general enough to predict that 1 data sample that wasn't included in the original modeling. This is one of the reasons why when having test size of 0.3, we have high scores.</p>
<p>5- Applied gridsearchCV with custom scoring function and DecisionTree classifier. (I varied criterion and splitter). I do not use F1 score. Instead, I create my own scoring function. My scoring function was to maximize the minimum of precision and recall. This helped improve the performance significantly. F1 score is harmonic mean of precision and recall, so it tries to balance the two. It doesn't maximize either, but tries to keep both high and equal to one another. F1 = 2/(1/recall + 1/precision). I use make_scorer in sklearn to make custom scoring function.</p>
<p>6- For cross-validation I used the same tester function as that in tester.py. Sklearn's cross validation function uses the default 0.3 split. About using smaller test size, in general smaller test_size implies overfitting. However, we can repeat this for all data points, so we are in a way using all the data. Further, in cases like this where the number of samples is low, using lower split actually avoids overfitting. Because in modeling, we are predicting only 1 data point, so the model has to be general enough to predict that 1 data sample that wasn't included in the original modeling. This is one of the reasons why when we have test size of 0.3, you have high scores. "precision_recall" is a function I wrote to calculate precision and recall. It returns precision and recall from precitions and actual value. I thinkm, due to large difference in number of POIs and non-POIs,  18 pois in 150 people, when test_size is 0.1, we are predicting 2 or 1 POIs among 15, with test_sizxe is 0.3, we have 3 to 6 POIs in testing set. So even we only get 2 of them right, we have precision and recall above 0.3. So the scores appear artifitally high for a poorer model, like grade inflation. With 0.1, the model has to be generalize well to predict 1 data point, and any error is amplified. Regardless, it makes more sense to use 0.1 for test_size in gridsearchCV because thats what is used in tester.py. Also, the parameters of model are calculated based on CV object provided to gridsearchCV, so the parameters that give best score for 0.1 test size may be different from those for 0.3</p>
<p>Here is what I do:
6.1- We first divide the data set into a training and testing set of, say, 70%-30%.</p>
<p>6.2- On the 70% of the data that is put in the training set we then run the grid_search to train the model using the StratifiedShuffleSplit. Within the 'sss' the data is again randomly divided into 90-10 percent training-test sets for 1000 iterations, each iteration trains a model on 90% of the training set and tests on 10% of the original training set.</p>
<p>6.3- Then, the model with the parameters that have performed best on average, is used to generate predictions on the the 30% of the original data set that was set aside for testing. The predictions of that model are then compared to the actual values of the dependent variable to score the final model.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline

<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">import</span> <span class="nn">pprint</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">threading</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">datetime</span>
<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;../ud120-projects/tools/&quot;</span><span class="p">)</span>
<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;../ud120-projects/final_project/&quot;</span><span class="p">)</span>

<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">RandomizedPCA</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">make_scorer</span>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
<span class="kn">from</span> <span class="nn">sklearn.grid_search</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">recall_score</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">precision_score</span>
<span class="kn">from</span> <span class="nn">feature_format</span> <span class="kn">import</span> <span class="n">featureFormat</span><span class="p">,</span> <span class="n">targetFeatureSplit</span>
<span class="kn">from</span> <span class="nn">tester</span> <span class="kn">import</span> <span class="n">dump_classifier_and_data</span>
<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">StratifiedShuffleSplit</span>
<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">train_test_split</span>
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<h1 id="Task-1:-Select-what-features-you'll-use.">Task 1: Select what features you'll use.<a class="anchor-link" href="#Task-1:-Select-what-features-you'll-use.">&#182;</a></h1>
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<p>features_list is a list of strings, each of which is a feature name.
The first feature must be "poi".</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">features_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;poi&#39;</span><span class="p">,</span><span class="s1">&#39;salary&#39;</span><span class="p">,</span> <span class="s1">&#39;bonus&#39;</span><span class="p">]</span>
<span class="n">features_list</span>
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<pre>[&apos;poi&apos;, &apos;salary&apos;, &apos;bonus&apos;]</pre>
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<p>Load the dictionary containing the dataset</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;../ud120-projects/final_project/final_project_dataset.pkl&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">data_file</span><span class="p">:</span>
    <span class="n">data_dict</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">data_file</span><span class="p">)</span>

<span class="n">pp</span> <span class="o">=</span> <span class="n">pprint</span><span class="o">.</span><span class="n">PrettyPrinter</span><span class="p">(</span><span class="n">indent</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="c1">#pp.pprint(data_dict)</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">total_number</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data_dict</span><span class="p">)</span>
<span class="n">total_poi_number</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span> <span class="n">x</span><span class="p">[</span><span class="s1">&#39;poi&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="bp">True</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data_dict</span><span class="o">.</span><span class="n">values</span><span class="p">()</span> <span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;Total number of data points: &quot;</span><span class="p">,</span> <span class="n">total_number</span>
<span class="k">print</span> <span class="s2">&quot;Total number of POI: &quot;</span><span class="p">,</span> <span class="n">total_poi_number</span><span class="p">,</span> <span class="s2">&quot; Percentage of POI: &quot;</span><span class="p">,</span> \
       <span class="nb">round</span><span class="p">(</span><span class="n">total_poi_number</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">*</span> <span class="n">total_number</span><span class="p">),</span> <span class="mi">4</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="s2">&quot;%&quot;</span>
<span class="k">print</span> <span class="s2">&quot;Total number of non-POI: &quot;</span><span class="p">,</span> <span class="n">total_number</span> <span class="o">-</span> <span class="n">total_poi_number</span><span class="p">,</span> \
       <span class="s2">&quot; Percentage of non-POI: &quot;</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="nb">round</span><span class="p">(</span><span class="n">total_poi_number</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">*</span> <span class="n">total_number</span><span class="p">),</span> <span class="mi">4</span><span class="p">))</span><span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="s2">&quot;%&quot;</span>
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<pre>Total number of data points:  146
Total number of POI:  18  Percentage of POI:  12.33 %
Total number of non-POI:  128  Percentage of non-POI:  87.67 %
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">my_chosen_feature_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;total_payments&#39;</span><span class="p">,</span> <span class="s1">&#39;total_stock_value&#39;</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">my_chosen_feature_list</span><span class="p">:</span>
    <span class="k">print</span> <span class="s2">&quot;Total number of missing values for feature &#39;&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;&#39; is: &quot;</span><span class="p">,</span> \
    <span class="nb">sum</span><span class="p">(</span> <span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;NaN&quot;</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data_dict</span><span class="o">.</span><span class="n">values</span><span class="p">()</span> <span class="p">)</span>

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<pre>Total number of missing values for feature &apos; total_payments &apos; is:  21
Total number of missing values for feature &apos; total_stock_value &apos; is:  20
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<h1 id="Task-2:-Remove-outliers">Task 2: Remove outliers<a class="anchor-link" href="#Task-2:-Remove-outliers">&#182;</a></h1>
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<h2 id="Step-#1">Step #1<a class="anchor-link" href="#Step-#1">&#182;</a></h2>
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<p>Here I will at first remove all data that did not have 'salary' information. In other words, I will remove the person whose 'salary' is 'NaN'.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">data_dict</span><span class="p">)</span>
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<pre>146</pre>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">data_dict_no_NaN</span> <span class="o">=</span> <span class="p">{</span><span class="n">name</span><span class="p">:</span><span class="n">data_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">data_dict</span> <span class="k">if</span> <span class="n">data_dict</span><span class="p">[</span><span class="n">name</span><span class="p">][</span><span class="s2">&quot;salary&quot;</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;NaN&quot;</span><span class="p">}</span>
<span class="nb">len</span><span class="p">(</span><span class="n">data_dict_no_NaN</span><span class="p">)</span>
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<pre>95</pre>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#data_dict_no_NaN</span>
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<h2 id="Step-#2">Step #2<a class="anchor-link" href="#Step-#2">&#182;</a></h2>
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<p>A new function draw_scatterplot() is created here to draw the scatter plot for the data. This visualizes where the outliers are.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Red dots in the scatter plot are POI&#39;s </span>
<span class="c1"># Blue dots are not POI&#39;s</span>
<span class="k">def</span> <span class="nf">draw_scatterplot</span><span class="p">(</span><span class="n">featurelist</span><span class="p">,</span> <span class="n">datadict</span><span class="p">):</span>
    <span class="n">data</span> <span class="o">=</span> <span class="n">featureFormat</span><span class="p">(</span><span class="n">datadict</span><span class="p">,</span> <span class="n">featurelist</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">point</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;red&#39;</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;blue&#39;</span>
        <span class="n">matplotlib</span><span class="o">.</span><span class="n">pyplot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span> <span class="n">point</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">point</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">color</span> <span class="o">=</span> <span class="n">color</span><span class="p">)</span>
    <span class="n">matplotlib</span><span class="o">.</span><span class="n">pyplot</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span> <span class="n">featurelist</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="p">)</span>
    <span class="n">matplotlib</span><span class="o">.</span><span class="n">pyplot</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span> <span class="n">featurelist</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="p">)</span>
    <span class="n">matplotlib</span><span class="o">.</span><span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>Draw the scatter plot for all data. Let us see the outliers. I am using 'salary' and 'bonus' as the features to study. As I mentioned in the code above, the red dots in the scatter plot are POI's, and the blue dots in the scatter plot are non-POI's</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">features_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;poi&#39;</span><span class="p">,</span><span class="s1">&#39;salary&#39;</span><span class="p">,</span> <span class="s1">&#39;bonus&#39;</span><span class="p">]</span>
<span class="n">draw_scatterplot</span><span class="p">(</span><span class="n">features_list</span><span class="p">,</span> <span class="n">data_dict_no_NaN</span><span class="p">)</span>
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<p>In the scatter plot above, a blue dot is found with much bigger bonus and salary. Now I need to find out who this is.</p>

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<p>Define the function Remove_Max_Outlier() to remove the outliers from the dataset.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="k">def</span> <span class="nf">Remove_Max_Outlier</span><span class="p">(</span><span class="n">datadict</span><span class="p">,</span> <span class="n">feature</span><span class="p">):</span>
    <span class="n">v</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">k</span><span class="p">[</span><span class="n">feature</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">datadict</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
    <span class="n">k</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">datadict</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
    <span class="n">name</span> <span class="o">=</span> <span class="n">k</span><span class="p">[</span><span class="n">v</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">v</span><span class="p">))]</span>
    <span class="k">print</span> <span class="n">name</span>
    <span class="k">print</span> <span class="n">datadict</span><span class="p">[</span><span class="n">name</span><span class="p">][</span><span class="n">feature</span><span class="p">]</span>

    <span class="k">del</span> <span class="n">datadict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
    <span class="k">return</span> <span class="n">datadict</span>
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<p>Now run this function to remove the outlier. It also print out some information about this outlier.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">data_dict_no_Outlier</span> <span class="o">=</span> <span class="n">Remove_Max_Outlier</span><span class="p">(</span><span class="n">data_dict_no_NaN</span><span class="p">,</span> <span class="s2">&quot;total_payments&quot;</span><span class="p">)</span>
<span class="c1">#data_dict_no_Outlier</span>
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<pre>TOTAL
309886585
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<p>Draw a scatter plot to see which outliers are removed. As I mentioned before, the red dots are POI's. the blue dots are non-POI's.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">draw_scatterplot</span><span class="p">(</span><span class="n">features_list</span><span class="p">,</span> <span class="n">data_dict_no_Outlier</span><span class="p">)</span>
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<h1 id="Task-3:-Change-the-value-of-data-to-get-more-normal-distribution">Task 3: Change the value of data to get more normal distribution<a class="anchor-link" href="#Task-3:-Change-the-value-of-data-to-get-more-normal-distribution">&#182;</a></h1>
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<h2 id="Step-#1,-Apply-log-(1+data)-to-financial-features-in-the-dataset">Step #1, Apply log (1+data) to financial features in the dataset<a class="anchor-link" href="#Step-#1,-Apply-log-(1+data)-to-financial-features-in-the-dataset">&#182;</a></h2>
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<p>Applied log(1+data) to financial information, and to email information to get more 'normal' distribution. I computed ratios and log-transformed them for email features, and took log of financial data.</p>

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<p>Now I need to set all data to be zero, if it is NaN. To do this, I define a function NaN_to_Zero().</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="k">def</span> <span class="nf">NaN_to_Zero</span><span class="p">(</span><span class="n">featurelist</span><span class="p">,</span> <span class="n">datadict</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">datadict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">featurelist</span><span class="p">:</span>
            <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">float</span> <span class="ow">and</span> <span class="n">math</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">value</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="ow">or</span> <span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">str</span> <span class="ow">and</span> <span class="n">value</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;NaN&quot;</span> <span class="p">:</span>
                <span class="n">datadict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">return</span> <span class="n">datadict</span>
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