|
|
@@ -22,7 +22,7 @@
|
|
|
"Collecting data for over 4 million apps requires a significant amount of time and money, so we'll try to analyze a sample of data instead. To avoid spending resources with collecting new data ourselves, we should first try to see whether we can find any relevant existing data at no cost. Luckily, these are two data sets that seem suitable for our purpose:\n",
|
|
|
"\n",
|
|
|
"- [A data set](https://www.kaggle.com/lava18/google-play-store-apps/home) containing data about approximately ten thousand Android apps from Google Play\n",
|
|
|
- "- [A data set](https://www.kaggle.com/lava18/google-play-store-apps/home) containing data about approximately seven thousand iOS apps from App Store\n",
|
|
|
+ "- [A data set](https://www.kaggle.com/ramamet4/app-store-apple-data-set-10k-apps/home) containing data about approximately seven thousand iOS apps from App Store\n",
|
|
|
"\n",
|
|
|
"Let's start by opening the two data sets and then continue with exploring the data."
|
|
|
]
|
|
|
@@ -144,7 +144,7 @@
|
|
|
"cell_type": "markdown",
|
|
|
"metadata": {},
|
|
|
"source": [
|
|
|
- "We have 7197 iOS apps in this data set, and the columns that seem interesting are: `'track_name'`, `'currency'`, `'price'`, `'rating_count_tot'`, `'rating_count_ver'`, and `'prime_genre'`. Not all column names are self-explanatory in this case, but details about each column can be found in the data set [documentation](https://www.kaggle.com/lava18/google-play-store-apps/home).\n",
|
|
|
+ "We have 7197 iOS apps in this data set, and the columns that seem interesting are: `'track_name'`, `'currency'`, `'price'`, `'rating_count_tot'`, `'rating_count_ver'`, and `'prime_genre'`. Not all column names are self-explanatory in this case, but details about each column can be found in the data set [documentation](https://www.kaggle.com/ramamet4/app-store-apple-data-set-10k-apps/home).\n",
|
|
|
"\n",
|
|
|
"\n",
|
|
|
"## Data Cleaning\n",
|
Alex