From bf0db688f00d6758a8a989a31278d4b8f14df9f0 Mon Sep 17 00:00:00 2001
From: wa2-alaaiddin <Wassem2.Alaaiddin@live.uwe.ac.uk>
Date: Tue, 13 Dec 2022 19:00:07 +0400
Subject: [PATCH] Task 2 Updated
---
UFCFVQ-15-M Programming Task 2.ipynb | 82 ++++++++++++++++++++++++----
1 file changed, 71 insertions(+), 11 deletions(-)
diff --git a/UFCFVQ-15-M Programming Task 2.ipynb b/UFCFVQ-15-M Programming Task 2.ipynb
index 8e11556..6f55334 100644
--- a/UFCFVQ-15-M Programming Task 2.ipynb
+++ b/UFCFVQ-15-M Programming Task 2.ipynb
@@ -1480,10 +1480,10 @@
"# agg() = Apply function/list of function over an axis of the Data\n",
"# reset_index() = Reset the index to default\n",
"\n",
- "UpdateDataFrame = UpdateDataFrame.groupby('age_band').agg({'final_mark':'mean', 'click_events':'mean'}).reset_index()\n",
+ "UpdateDataFrameMean = UpdateDataFrame.groupby('age_band').agg({'final_mark':'mean', 'click_events':'mean'}).reset_index()\n",
"\n",
"# Printing the Output\n",
- "UpdateDataFrame"
+ "UpdateDataFrameMean"
]
},
{
@@ -1506,7 +1506,7 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -1532,11 +1532,12 @@
],
"source": [
"# Visualisating the final marks respecting to each age band\n",
- "sns.catplot(x=\"age_band\", y=\"final_mark\",hue=\"final_mark\",data=UpdateDataFrame,\n",
+ "sns.catplot(x=\"age_band\", y=\"final_mark\",hue=\"final_mark\",data=UpdateDataFrameMean,\n",
" kind=\"bar\", palette=\"Accent_r\").set(title='Final Marks in Each Age Band')\n",
"plt.show()\n",
+ "\n",
"# Visualisating the click_events respecting to each age band\n",
- "sns.catplot(x=\"age_band\", y=\"click_events\",hue=\"click_events\", data=UpdateDataFrame,\n",
+ "sns.catplot(x=\"age_band\", y=\"click_events\",hue=\"click_events\", data=UpdateDataFrameMean,\n",
" kind=\"bar\", palette=\"Accent_r\" ).set(title='Engagement in Each Age Band')\n",
"plt.show()"
]
@@ -1550,11 +1551,36 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 49,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "# add code here"
+ "#drooping all columns exept 'final grade' and 'click events'\n",
+ "CorrFinder = UpdateDataFrame.drop(['id_student','disability','age_band','gender','Unnamed: 0','Unnamed: 0.1'], axis=1)\n",
+ "\n",
+ "#using corr() function to find the correlation between the two columns. and visualizing it as a heatmap.\n",
+ "sns.heatmap(CorrFinder.corr(), annot=True, cmap='cool')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- According to the heatmap, the correlation between both columns are 0.27, therefore it indicates a weak positive (negative) linear relationship via a shaky linear rule. \n",
+ "\n",
+ "- Heatmap is used as it visualize the result in the best format for the sys to understand."
]
},
{
@@ -1566,11 +1592,38 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The p-value is 0.0 therfore, reject null hypothesis\n"
+ ]
+ }
+ ],
+ "source": [
+ "#importing stats from scipy\n",
+ "from scipy import stats\n",
+ "\n",
+ "#p_value = probability that the null hypothesis is true.\n",
+ "#collecting out the p_value of 'final_mark' and 'click_events' using stats formaula. \n",
+ "\n",
+ "Ttest,Pvalue = stats.ttest_ind(CorrFinder['final_mark'], CorrFinder['click_events'])\n",
+ "\n",
+ "#If the p value is > 0.05 or less than 0, accept null hypothesis else reject null hypothesis\n",
+ "if Pvalue >= 0 and Pvalue <= 0.05:\n",
+ " print(f\"The p-value is {Pvalue} therfore, reject null hypothesis\")\n",
+ "else:\n",
+ " print(f\"The p-value is {Pvalue} therfore, accept null hypothesis\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "# add code here"
+ "- The p-value is 0.0 which is in a range of 0.00 and 0.05.. (not null hypothesis). Therfore; there is a relationship between the two phenomena and the results are <b>statistically significant</b>."
]
},
{
@@ -1579,6 +1632,13 @@
"source": [
"# Process Development Report for Task 2\n"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#Report"
+ ]
}
],
"metadata": {
--
GitLab