diff --git a/features/generate_dataset.py b/features/generate_dataset.py
new file mode 100644
index 0000000..b13ae88
--- /dev/null
+++ b/features/generate_dataset.py
@@ -0,0 +1,123 @@
+"""This module is used to go from raw data to a csv dataset to build models for
+   component prediction.
+"""
+from __future__ import print_function
+from __future__ import division
+from __future__ import absolute_import
+
+import argparse
+import string
+import sys
+import csv
+import re
+import logging
+import random
+import time
+import os
+import settings
+from framework import sql
+from framework import servlet
+
+if not settings.unit_test_mode:
+  import MySQLdb as mdb
+ISSUE_LIMIT = 7000
+ISSUES_PER_RUN = 50
+COMPONENT_PREDICTOR_PROJECT = 16
+
+def build_component_dataset(issue, csv_file):
+  """Main function to build dataset for training models.
+
+  Args:
+    issue: The issue service with set up data.
+    csv_file: The csv file path to store the dataset.
+  """
+
+  logging.info('Building dataset')
+  con = sql.MonorailConnection()
+
+  csv_writer = csv.writer(csv_file)
+
+  logging.info('Downloading the dataset from database.')
+
+  issue_table = sql.SQLTableManager('Issue')
+  issue_component_table = sql.SQLTableManager('Issue2Component')
+  closed_index_table = sql.SQLTableManager('ComponentIssueClosedIndex')
+
+  close = closed_index_table.SelectValue(con, col='closed_index')
+
+  last_close = issue_table.Select(con,
+                                  cols=['closed'],
+                                  where=[('closed > %s', [str(close)]),
+                                         ('project_id = %s',
+                                          [str(COMPONENT_PREDICTOR_PROJECT)])],
+                                  order_by=[('closed', [])],
+                                  limit=ISSUE_LIMIT)[-1][0]
+
+  issue_ids = issue_table.Select(con,
+                              cols=['id'],
+                              where=[('closed > %s', [str(close)]),
+                                     ('closed <= %s', [str(last_close)]),
+                                     ('project_id = %s',
+                                      [str(COMPONENT_PREDICTOR_PROJECT)])])
+
+
+  logging.info('Close: ' + str(close))
+  logging.info('Last close: ' + str(last_close))
+
+  # Get the comments and components for 50 issues at a time so as to not
+  # overwhelm a single shard with all 7000 issues at once
+  for i in range(0, len(issue_ids), ISSUES_PER_RUN):
+    issue_list = [str(x[0]) for x in issue_ids[i:i+ISSUES_PER_RUN]]
+
+    comments = issue.GetCommentsForIssues(con, issue_list, content_only=True)
+
+    shard_id = random.randint(0, settings.num_logical_shards - 1)
+
+    components = issue_component_table.Select(con,
+                                        cols=['issue_id',
+                                              'GROUP_CONCAT(component_id '
+                                              + 'SEPARATOR \',\')'],
+                                        joins=[('ComponentDef ON '
+                                                'ComponentDef.id = '
+                                                'Issue2Component.component_id',
+                                                [])],
+                                        where=[('(deprecated = %s OR deprecated'
+                                                ' IS NULL)', [False]),
+                                                ('is_deleted = %s', [False])],
+                                        group_by=['issue_id'],
+                                        shard_id=shard_id,
+                                        issue_id=issue_list)
+
+    for issue_id, component_ids in components:
+      comment_string = ' '.join(
+          [comment.content for comment in comments[issue_id]])
+
+      final_text = CleanText(comment_string)
+
+      final_issue = component_ids, final_text
+      csv_writer.writerow(final_issue)
+
+  closed_index_table.Update(con, delta={'closed_index' : last_close})
+
+  return csv_file
+
+
+def CleanText(text):
+  """Cleans provided text by lower casing words, removing punctuation, and
+  normalizing spacing so that there is exactly one space between each word.
+
+  Args:
+    text: Raw text to be cleaned.
+
+  Returns:
+    Cleaned version of text.
+
+  """
+
+  pretty_issue = text.lower().strip()
+
+  quoteless_issue = re.sub('\'', '', pretty_issue)
+  no_punctuation_issue = re.sub('[^\w\s]|_+', ' ', quoteless_issue)
+  one_space_issue = ' '.join(no_punctuation_issue.split())
+
+  return one_space_issue