This Project comprises of Logistic Regression exercise in python.
Data Head
| Daily Time Spent on Site | Age | Area Income | Daily Internet Usage | Ad Topic Line | City | Male | Country | Timestamp | Clicked on Ad | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 68.95 | 35 | 61833.90 | 256.09 | Cloned 5thgeneration orchestration | Wrightburgh | 0 | Tunisia | 2016-03-27 00:53:11 | 0 |
| 1 | 80.23 | 31 | 68441.85 | 193.77 | Monitored national standardization | West Jodi | 1 | Nauru | 2016-04-04 01:39:02 | 0 |
| 2 | 69.47 | 26 | 59785.94 | 236.50 | Organic bottom-line service-desk | Davidton | 0 | San Marino | 2016-03-13 20:35:42 | 0 |
| 3 | 74.15 | 29 | 54806.18 | 245.89 | Triple-buffered reciprocal time-frame | West Terrifurt | 1 | Italy | 2016-01-10 02:31:19 | 0 |
| 4 | 68.37 | 35 | 73889.99 | 225.58 | Robust logistical utilization | South Manuel | 0 | Iceland | 2016-06-03 03:36:18 | 0 |
** Crude data metrics**
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 10 columns):
Daily Time Spent on Site 1000 non-null float64
Age 1000 non-null int64
Area Income 1000 non-null float64
Daily Internet Usage 1000 non-null float64
Ad Topic Line 1000 non-null object
City 1000 non-null object
Male 1000 non-null int64
Country 1000 non-null object
Timestamp 1000 non-null object
Clicked on Ad 1000 non-null int64
dtypes: float64(3), int64(3), object(4)
memory usage: 78.2+ KB
| Daily Time Spent on Site | Age | Area Income | Daily Internet Usage | Male | Clicked on Ad | |
|---|---|---|---|---|---|---|
| count | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.00000 |
| mean | 65.000200 | 36.009000 | 55000.000080 | 180.000100 | 0.481000 | 0.50000 |
| std | 15.853615 | 8.785562 | 13414.634022 | 43.902339 | 0.499889 | 0.50025 |
| min | 32.600000 | 19.000000 | 13996.500000 | 104.780000 | 0.000000 | 0.00000 |
| 25% | 51.360000 | 29.000000 | 47031.802500 | 138.830000 | 0.000000 | 0.00000 |
| 50% | 68.215000 | 35.000000 | 57012.300000 | 183.130000 | 0.000000 | 0.50000 |
| 75% | 78.547500 | 42.000000 | 65470.635000 | 218.792500 | 1.000000 | 1.00000 |
| max | 91.430000 | 61.000000 | 79484.800000 | 269.960000 | 1.000000 | 1.00000 |
Data Exploration
** Histogram of the Age**
<matplotlib.axes._subplots.AxesSubplot at 0x7f9ba54bf510>
Jointplot showing Area Income versus Age.
<seaborn.axisgrid.JointGrid at 0x7f9ba5189750>
Jointplot showing the kde distributions of Daily Time spent on site vs. Age.
<seaborn.axisgrid.JointGrid at 0x7f9ba2d23590>
** Jointplot of 'Daily Time Spent on Site' vs. 'Daily Internet Usage'**
<seaborn.axisgrid.JointGrid at 0x7f9ba2b88150>
** Pairplot with the hue defined by the 'Clicked on Ad' column feature.**
<seaborn.axisgrid.PairGrid at 0x7f9ba2aad210>
Model Evaluation
** Classification report for the model.**
precision recall f1-score support
0 0.91 0.95 0.93 157
1 0.94 0.90 0.92 143
avg / total 0.92 0.92 0.92 300
** Confusion Matrix **
[[149 8]
[ 15 128]]
